import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import mpl_toolkits as mplot3d
import scipy as sp
pd.set_option("display.max_columns", None)
from tqdm import tqdm
tqdm.pandas()

def lighten_color(color, amount=0.5):
    """
    Lightens the given color by multiplying (1-luminosity) by the given amount.
    Input can be matplotlib color string, hex string, or RGB tuple.

    Examples:
    >> lighten_color('g', 0.3)
    >> lighten_color('#F034A3', 0.6)
    >> lighten_color((.3,.55,.1), 0.5)
    """
    import matplotlib.colors as mc
    import colorsys
    try:
        c = mc.cnames[color]
    except:
        c = color
    c = colorsys.rgb_to_hls(*mc.to_rgb(c))
    return colorsys.hls_to_rgb(c[0], 1 - amount * (1 - c[1]), c[2])

d0 = pd.read_excel("risk literacy_985 responses.xlsx", sheet_name = "643 above median").rename({"#":"ResponseId"}, axis = 1)
# d0 = d0.loc[ (d0["Please state your current occupation."] != "Student") ]
d0

d0["Please state your current occupation."].value_counts()

Please state your current occupation.
Employee/Consultant in Other than the Finance Sector                                                                                                                                        378
Investment Professional, i.e. stock broker/trader; financial planner/advisor; portfolio manager; investment banker; stock analyst; venture capital/private equity; insurance agent, etc.    154
Entrepreneur or Own Business                                                                                                                                                                 96
Student                                                                                                                                                                                       7
Home Maker or not employed                                                                                                                                                                    5
Retired person                                                                                                                                                                                3
Name: count, dtype: int64

# Age groups/rec = [2,3,4]
# Age group 2 = 18 to 35 y/o
# Age group 3 = 36 to 55 y/o
# Age group 4 = 56 to 75 y/o (75 y/o, i.e, within the scope of the data we have, it can mean 60 and above also)

d0["age_rec"] = np.where( d0["Please mark your age (in years)"] == "18 - 25" , 2,
                          np.where(d0["Please mark your age (in years)"] == "25 - 35", 2,
                                   np.where( d0["Please mark your age (in years)"] == "36 - 45", 3,
                                            np.where( d0["Please mark your age (in years)"] == "46 - 55", 3,
                                                     np.where( d0["Please mark your age (in years)"] == "56 - 65", 4,
                                                              np.where( d0["Please mark your age (in years)"] == "Above 65", 4, 0
                                                                      )
                                                             )
                                                    )
                                           )
                                  )
                        )

d0["age"] = np.where( d0["Please mark your age (in years)"] == "18 - 25" , (18 + 25) / 2,
                          np.where(d0["Please mark your age (in years)"] == "25 - 35", (26 + 35) / 2,
                                   np.where( d0["Please mark your age (in years)"] == "36 - 45", (36 + 45) / 2,
                                            np.where( d0["Please mark your age (in years)"] == "46 - 55", (46 + 55) / 2,
                                                     np.where( d0["Please mark your age (in years)"] == "56 - 65", (56 + 65) / 2,
                                                              np.where( d0["Please mark your age (in years)"] == "Above 65", (66 + 75) / 2, 0
                                                                      )
                                                             )
                                                    )
                                           )
                                  )
                        )


# Secondary upto 10 – ISCED 3
# Senior Secondary upto 12 - ISCED 3 
# Diploma and voca – ISCED 2
# UG – ISCED 2
# PG – ISCED 1
# PhD and higher – ISCED 1

d0["isced"] = np.where(d0["What is currently your highest Education?"] == "Post Graduate", 1,
                        np.where(d0["What is currently your highest Education?"] == "Under Graduate", 2,
                                 np.where(d0["What is currently your highest Education?"] == "Ph.D. or higher", 1,
                                          np.where(d0["What is currently your highest Education?"] == "Diploma or vocation training", 2,
                                                   np.where(d0["What is currently your highest Education?"] == "School degree (X or XII)", 3, 0
                                                           )
                                                  )
                                         )
                                )
                       )

d0["income"] = np.where(d0["Please indicate your approximate annual personal income from all sources for last year"] == "&lt; 5,00,000", 1,
                         np.where(d0["Please indicate your approximate annual personal income from all sources for last year"] == "5,00,000 - 15,00,000", 2,
                                  np.where(d0["Please indicate your approximate annual personal income from all sources for last year"] == "15,00,001 - 25,00,000", 3,
                                           np.where(d0["Please indicate your approximate annual personal income from all sources for last year"] == "25,00,001 - 35,00,000", 4,
                                                    np.where(d0["Please indicate your approximate annual personal income from all sources for last year"] == "35,00,001 - 45,00,000", 4,
                                                             np.where( d0["Please indicate your approximate annual personal income from all sources for last year"] == "&gt; 45,00,000", 5,0
                                                                     )
                                                            )
                                                   )
                                           )
                                  )
                         )

d0["wealth"] = d0["Please provide a rough guess (in Indian Rupees) of the worth of your household's assets. Please do not forget to correct it for your debts, such as a mortgage or any loans you might have."]

matchCol = ["q8_2_1", "q8_2_2", "q8_2_3", "q8_2_4", "q8_2_5", "q8_3", "q8_4", "q8_5berlin_1", 
            "q8_5london_1", "q8_5paris_1", "q8_6", "q8_7", "q9_1_1", "q9_2_1", "q9_3",
            "q10_1_1", "q10_2_1", "q10_3_1", "q10_4"]
d0[ matchCol ] = 0

d0.columns

Index(['ResponseId', 'Please indicate your Gender.',
       'Please mark your age (in years)',
       'What is currently your highest Education?',
       'Please state your current occupation.',
       'How do you describe your willingness to take financial risk in general?',
       'Given the number of years that you have held various investments and the amount of investing you might have done, what degree of investment experience in the stock market do you have?',
       'If an expert tries to worry or scare me, i.e. a financial advisor about my financial situation, I choose another expert.',
       'I only buy a financial product I understand.', 'I trust doctors.',
       'When I want to buy a bigger item like a refrigerator or an expensive item of clothing, I wait a month to see whether I still want it and only buy it then.',
       'I always keep in mind that everything I do on the web could be used to my disadvantage.',
       'In my household, we/I spend:', 'In my household, we/I',
       '_Distribution 1_\n\nHow risky do you perceive the investment to be?',
       '_Distribution 2_\n\nHow risky do you perceive the investment to be?',
       '_Distribution 3_\n\nHow risky do you perceive the investment to be?',
       '_Distribution 4_\n\nHow risky do you perceive the investment to be?',
       '_Distribution 5_\n\nHow risky do you perceive the investment to be?',
       'Distribution 6\n\nHow risky do you perceive the investment to be?',
       'Distribution 7\n\nHow risky do you perceive the investment to be?',
       'Distribution 8\n\nHow risky do you perceive the investment to be?',
       '*Mumbai * A = 9 out of 10000   *OR   * B = 1 out of 1000',
       '*Bengaluru * A = 0.7%   *OR  * B = 0.099%',
       '*Kolkata * A = 0.61%   *OR   * B = 6 out of 10000', 'HIV test',
       'Fingerprint', 'DNA test', 'Cancer screening test',
       'Professional horoscope',
       'A study estimates that eating 100g chocolate everyday increases the risk of obesity by 20%.  Which of the following statements is true?',
       'There is an official prediction that the national stock market will grow 2% annually over the next 5 years.  This means that…',
       'Imagine you are told that the price of the stock Soya Ruchi increases from INR 60 to INR 120 after the company merger. What does this mean?',
       'It is predicted that Indigo Bank has 30% chance of default next year. Which of the following alternatives is the most appropriate interpretation of the statement?',
       'The probability that the economy will go into a recession this year is 30%. If the economy goes into recession, the probability that the stock market will decrease is 80%. If the economy does not go into a recession, the probability that the stock market will decrease is 23%. What is the probability that the economy goes into recession given that the stock market decreased?',
       'A new policy intervention increases the number of people who are employed by 20%. This statistic implies that the intervention increases the number of people who are employed from:',
       'Imagine you are told that a new medication increases the number of people who recover from a disease from 2 out of 1,000 to 4 out of 1,000.  This implies:',
       'Imagine that we flip a fair coin 1,000 times. What is your best guess about how many times the coin will come up heads in 1,000 flips? \n\n\_\_\_\_\_\_ times out of 1,000.',
       'In the Bingo Lottery, the chance of winning a $10 prize is 1%. What is your best guess about how many people will win a $10 prize if 1,000 people each buy a single ticket for Bingo Lottery?\n\n\_\_\_\_\_\_ person(s) out of 1,000.',
       'In a sweepstakes, the chance of winning a car is 1 in 1,000. What percentage of tickets for the sweepstakes wins a car?\n\n\_\_\_\_\_ % of tickets',
       'About 10 out of 1,000 children develop Down syndrome. In a Down syndrome test, 9 out of these 10 children with Down syndrome tested positive. Out of the 990 children without Down syndrome 50 nevertheless tested positive. Among those women with a positive test result concerning their child how many actually have a child with Down syndrome? [Select one response only]',
       'Approximately what percentage (%) of people who die from cancer die from colon cancer, breast cancer, and prostate cancer taken together?',
       'The following figure shows the number of men and women among a group of smartphone users. The total number of circles is 100. \n\nHow many more men than women are there among the 100 people using a smartphone?',
       'In a magazine you see two advertisements, one on page 5 and another on page 12. Each is for a different drug for treating heart disease, and each includes a graph showing the effectiveness of the drug compared to a placebo (sugar pill).\n\nCompared to the placebo, which treatment leads to a larger decrease in the percentage of patients who die?',
       'Please indicate your approximate annual personal income from all sources for last year',
       'Please provide a rough guess (in Indian Rupees) of the worth of your household's assets. Please do not forget to correct it for your debts, such as a mortgage or any loans you might have.',
       'uid', 'Response Type', 'Start Date (UTC)', 'Stage Date (UTC)',
       'Submit Date (UTC)', 'Network ID', 'LTA', 'age_rec', 'age', 'isced',
       'income', 'wealth', 'q8_2_1', 'q8_2_2', 'q8_2_3', 'q8_2_4', 'q8_2_5',
       'q8_3', 'q8_4', 'q8_5berlin_1', 'q8_5london_1', 'q8_5paris_1', 'q8_6',
       'q8_7', 'q9_1_1', 'q9_2_1', 'q9_3', 'q10_1_1', 'q10_2_1', 'q10_3_1',
       'q10_4'],
      dtype='object')

d0["HIV test"].value_counts()

HIV test
Yes    436
No     207
Name: count, dtype: int64

d0["q8_2_1"] = np.where(d0["HIV test"] == "Yes", 1, 2)
d0["q8_2_2"] = np.where(d0["Fingerprint"] == "Yes", 1, 2)
d0["q8_2_3"] = np.where(d0["DNA test"] == "Yes", 1, 2)
d0["q8_2_4"] = np.where(d0["Cancer screening test"] == "Yes", 1, 2)
d0["q8_2_5"] = np.where(d0["Professional horoscope"] == "Yes", 1, 2)

d0["q8_3"] = np.where(d0["A study estimates that eating 100g chocolate everyday increases the risk of obesity by 20%.  Which of the following statements is true?"] == "The lower the quality of the study, the more likely that future studies will change the risk estimate.", 1,
                          np.where(d0["A study estimates that eating 100g chocolate everyday increases the risk of obesity by 20%.  Which of the following statements is true?"] == "The higher the quality of the study, the more likely that future studies will change the risk estimate.", 2,
                                  np.where(d0["A study estimates that eating 100g chocolate everyday increases the risk of obesity by 20%.  Which of the following statements is true?"] == "Irrespective of the quality of the study, future studies will not change the risk estimate.", 3,
                                          np.where(d0["A study estimates that eating 100g chocolate everyday increases the risk of obesity by 20%.  Which of the following statements is true?"] == "Irrespective of the quality of the study, future studies will change the risk estimate substantially anyway.", 4,0
                                                  )
                                          )
                                  )
                         )


d0["q8_4"] = np.where(d0["There is an official prediction that the national stock market will grow 2% annually over the next 5 years.  This means that…"] == "the growth rate will be 0.4% on average each year", 1,
                          np.where(d0["There is an official prediction that the national stock market will grow 2% annually over the next 5 years.  This means that…"] == "the growth rate over five years will be exactly 2%", 2,
                                  np.where(d0["There is an official prediction that the national stock market will grow 2% annually over the next 5 years.  This means that…"] == "the growth rate over five years will be between 1% and 3%", 3,
                                          np.where(d0["There is an official prediction that the national stock market will grow 2% annually over the next 5 years.  This means that…"] == "it is not possible to predict the growth rate with certainty.", 4,0
                                                  )
                                          )
                                  )
                         )

d0["q8_5berlin_1"] = np.where(d0["*Mumbai * A = 9 out of 10000   *OR   * B = 1 out of 1000"] == "A", 1, 2)
d0["q8_5london_1"] = np.where(d0["*Bengaluru * A = 0.7%   *OR  * B = 0.099%"] == "A", 1, 2)
d0["q8_5paris_1"] = np.where(d0["*Kolkata * A = 0.61%   *OR   * B = 6 out of 10000"] == "A", 1, 2)

d0["q8_6"] = np.where(d0["Imagine you are told that a new medication increases the number of people who recover from a disease from 2 out of 1,000 to 4 out of 1,000.  This implies:"] == "The medication increases recovery by 100%", 1,
                          np.where(d0["Imagine you are told that a new medication increases the number of people who recover from a disease from 2 out of 1,000 to 4 out of 1,000.  This implies:"] == "The medication increases recovery by 50%", 2,
                                   np.where(d0["Imagine you are told that a new medication increases the number of people who recover from a disease from 2 out of 1,000 to 4 out of 1,000.  This implies:"] == "The medication increases recovery by 2%", 3,
                                            np.where(d0["Imagine you are told that a new medication increases the number of people who recover from a disease from 2 out of 1,000 to 4 out of 1,000.  This implies:"] == "None of the above is implied.", 4, 0
                                                    )
                                           )
                                  )
                         )

d0["q8_7"] = np.where(d0["A new policy intervention increases the number of people who are employed by 20%. This statistic implies that the intervention increases the number of people who are employed from:"] == "5 in 100 people prior to the intervention to 6 out of 100 people after the intervention", 1,
                          np.where(d0["A new policy intervention increases the number of people who are employed by 20%. This statistic implies that the intervention increases the number of people who are employed from:"] == "100 in 10,000 people prior to the intervention to 120 out of 10,000 people after the intervention", 2,
                                   np.where(d0["A new policy intervention increases the number of people who are employed by 20%. This statistic implies that the intervention increases the number of people who are employed from:"] == "70 in 100 people prior to the intervention to 90 out of 100 people after the intervention", 3,
                                            np.where(d0["A new policy intervention increases the number of people who are employed by 20%. This statistic implies that the intervention increases the number of people who are employed from:"] == "it is not possible to determine which of the answers is correct given the information provided", 4, 0
                                                    )
                                           )
                                  )
                         )


d0["q9_1_1"] = d0["Approximately what percentage (%) of people who die from cancer die from colon cancer, breast cancer, and prostate cancer taken together?"].copy()
d0["q9_2_1"] = d0["The following figure shows the number of men and women among a group of smartphone users. The total number of circles is 100. \n\nHow many more men than women are there among the 100 people using a smartphone?"].copy()

d0["q9_3"] = np.where(d0["In a magazine you see two advertisements, one on page 5 and another on page 12. Each is for a different drug for treating heart disease, and each includes a graph showing the effectiveness of the drug compared to a placebo (sugar pill).\n\nCompared to the placebo, which treatment leads to a larger decrease in the percentage of patients who die?"] == "Crosicol", 1,
                          np.where(d0["In a magazine you see two advertisements, one on page 5 and another on page 12. Each is for a different drug for treating heart disease, and each includes a graph showing the effectiveness of the drug compared to a placebo (sugar pill).\n\nCompared to the placebo, which treatment leads to a larger decrease in the percentage of patients who die?"] == "Hertinol", 2,
                                   np.where(d0["In a magazine you see two advertisements, one on page 5 and another on page 12. Each is for a different drug for treating heart disease, and each includes a graph showing the effectiveness of the drug compared to a placebo (sugar pill).\n\nCompared to the placebo, which treatment leads to a larger decrease in the percentage of patients who die?"] == "They are equal", 3,
                                            np.where(d0["In a magazine you see two advertisements, one on page 5 and another on page 12. Each is for a different drug for treating heart disease, and each includes a graph showing the effectiveness of the drug compared to a placebo (sugar pill).\n\nCompared to the placebo, which treatment leads to a larger decrease in the percentage of patients who die?"] == "Can’t say", 4, 0
                                                    )
                                           )
                                  )
                         )

d0["q10_1_1"] = d0["Imagine that we flip a fair coin 1,000 times. What is your best guess about how many times the coin will come up heads in 1,000 flips? \n\n\_\_\_\_\_\_ times out of 1,000."].copy()
d0["q10_2_1"] = d0["In the Bingo Lottery, the chance of winning a $10 prize is 1%. What is your best guess about how many people will win a $10 prize if 1,000 people each buy a single ticket for Bingo Lottery?\n\n\_\_\_\_\_\_ person(s) out of 1,000."].copy()
d0["q10_3_1"] = d0["In a sweepstakes, the chance of winning a car is 1 in 1,000. What percentage of tickets for the sweepstakes wins a car?\n\n\_\_\_\_\_ % of tickets"].copy()

d0["q10_4"] = np.where(d0["About 10 out of 1,000 children develop Down syndrome. In a Down syndrome test, 9 out of these 10 children with Down syndrome tested positive. Out of the 990 children without Down syndrome 50 nevertheless tested positive. Among those women with a positive test result concerning their child how many actually have a child with Down syndrome? [Select one response only]"] == "9 out of 59", 1,
                          np.where(d0["About 10 out of 1,000 children develop Down syndrome. In a Down syndrome test, 9 out of these 10 children with Down syndrome tested positive. Out of the 990 children without Down syndrome 50 nevertheless tested positive. Among those women with a positive test result concerning their child how many actually have a child with Down syndrome? [Select one response only]"] == "9 out of 10", 2,
                                   np.where(d0["About 10 out of 1,000 children develop Down syndrome. In a Down syndrome test, 9 out of these 10 children with Down syndrome tested positive. Out of the 990 children without Down syndrome 50 nevertheless tested positive. Among those women with a positive test result concerning their child how many actually have a child with Down syndrome? [Select one response only]"] == "59 out of 1000", 3,
                                            np.where(d0["About 10 out of 1,000 children develop Down syndrome. In a Down syndrome test, 9 out of these 10 children with Down syndrome tested positive. Out of the 990 children without Down syndrome 50 nevertheless tested positive. Among those women with a positive test result concerning their child how many actually have a child with Down syndrome? [Select one response only]"] == "59 out of 100", 4, 0
                                                    )
                                           )
                                  )
                         )

scoreColumns = ["certainty1", "certainty2", "certainty3", "certainty4", "certainty5", "uncertainty1", "uncertainty2", "numeracy1", "numeracy2", "numeracy3", "numeracy4", "numeracy5", "graph1", "graph2", "graph3", "riskcomprehension1", "riskcomprehension2", "riskcomprehension3", "riskcomprehension4", "riskcomprehension5", "bayesian1"]
d0[scoreColumns] = 0

# Assigning scores

def scoring1(surveyFacet):
    surveyFacet.loc[ surveyFacet["q8_2_1"] == 2, "certainty1"] = 1
    surveyFacet.loc[ surveyFacet["q8_2_2"] == 2, "certainty2"] = 1
    surveyFacet.loc[ surveyFacet["q8_2_3"] == 2, "certainty3"] = 1
    surveyFacet.loc[ surveyFacet["q8_2_4"] == 2, "certainty4"] = 1
    surveyFacet.loc[ surveyFacet["q8_2_5"] == 2, "certainty5"] = 1

    surveyFacet.loc[ surveyFacet["q8_3"] == 1, "uncertainty1"] = 1
    surveyFacet.loc[ surveyFacet["q8_4"] == 4, "uncertainty2"] = 1

    surveyFacet.loc[ surveyFacet["q8_5berlin_1"] == 2, "riskcomprehension1"] = 1
    surveyFacet.loc[ surveyFacet["q8_5london_1"] == 1, "riskcomprehension2"] = 1
    surveyFacet.loc[ surveyFacet["q8_5paris_1"] == 1, "riskcomprehension3"] = 1
    
    surveyFacet.loc[ surveyFacet["q8_6"] == 1, "riskcomprehension4"] = 1
    surveyFacet.loc[ surveyFacet["q8_7"] == 4, "riskcomprehension5"] = 1

    surveyFacet.loc[ surveyFacet["q9_1_1"] == 25, "graph1"] = 1
    surveyFacet.loc[ surveyFacet["q9_2_1"] == 20, "graph2"] = 1
    surveyFacet.loc[ surveyFacet["q9_3"] == 3, "graph3"] = 1

    surveyFacet.loc[ surveyFacet["q10_1_1"] == 500, "numeracy1"] = 1
    surveyFacet.loc[ surveyFacet["q10_2_1"] == 10, "numeracy2"] = 1
    surveyFacet.loc[ (surveyFacet["q10_3_1"] == 0.1) | (surveyFacet["q10_3_1"] == ".1") | (surveyFacet["q10_3_1"] == ",1") , "numeracy3"] = 1
    surveyFacet.loc[ surveyFacet["q10_4"] == 1, "bayesian1"] = 1

    # Assigning total scores
    # surveyFacet["Certainty score_5"] = surveyFacet["certainty1"] + surveyFacet["certainty2"] + surveyFacet["certainty3"] + surveyFacet["certainty4"] + surveyFacet["certainty5"]
    #surveyFacet["Uncertainty score_5"] = surveyFacet["uncertainty1"] + surveyFacet["uncertainty2"]

    #surveyFacet["Number Comprehension score_5"] = surveyFacet["numeracy1"] + surveyFacet["numeracy2"] + surveyFacet["numeracy3"] + surveyFacet["numeracy4"] + surveyFacet["numeracy5"]
    #surveyFacet["Graph Comprehension score_5"] = surveyFacet["graph1"] + surveyFacet["graph2"] + surveyFacet["graph3"]

    #surveyFacet["Calculation score_4"] = surveyFacet["riskcalculation1"] + surveyFacet["riskcalculation2"] + surveyFacet["riskcalculation3"] + surveyFacet["riskcalculation4"]

    #surveyFacet["Total Score_19"] = surveyFacet["Certainty score_5"] + surveyFacet["Uncertainty score_5"] + surveyFacet["Number Comprehension score_5"] + surveyFacet["Graph Comprehension score_5"] + surveyFacet["Calculation score_4"]

    return surveyFacet

d0 = d0.groupby(["ResponseId"]).progress_apply(scoring1)
d0

100%|███████████████████████████████████████████████████████████████████████████████| 643/643 [00:03<00:00, 171.80it/s]

def scoring2(survey):
    
    survey["Certainty_5"] = survey["certainty1"] + survey["certainty2"] + survey["certainty3"] + survey["certainty4"] + survey["certainty5"]
    
    survey["Uncertainty_2"] = survey["uncertainty1"] + survey["uncertainty2"]
    
    survey["RiskComprehension_5"] = survey["riskcomprehension1"] + survey["riskcomprehension2"] + survey["riskcomprehension3"] + survey["riskcomprehension4"] + survey["riskcomprehension5"]
    
    survey["GraphLiteracy_3"] = survey["graph1"] + survey["graph2"] + survey["graph3"]
    
    survey["Numeracy_2"] = survey["numeracy1"] + survey["numeracy2"] # + survey["numeracy3"]
    
    survey["Bayesianreasoning_1"] = survey["bayesian1"]
    
    survey["TotalScore_18"] = survey["Certainty_5"] + survey["Uncertainty_2"] + survey["RiskComprehension_5"] + survey["GraphLiteracy_3"] + survey["Numeracy_2"] + survey["Bayesianreasoning_1"]
    
    survey["Certainty_%"] = survey["Certainty_5"] / 5 * 100
    survey["Uncertainty_%"] = survey["Uncertainty_2"] / 2 * 100
    survey["RiskComprehension_%"] = survey["RiskComprehension_5"] / 5 * 100
    survey["GraphLiteracy_%"] = survey["GraphLiteracy_3"] / 3 * 100
    survey["Numeracy_%"] = survey["Numeracy_2"] / 2 * 100
    survey["Bayesianreasoning_%"] = survey["Bayesianreasoning_1"] / 1 * 100
    
    survey["TotalScore_%"] = survey["TotalScore_18"] / 18 * 100
    
    
    colReq = ["ResponseId", "age", "age_rec", "isced", "income", "wealth", "Certainty_5", "Uncertainty_2", "RiskComprehension_5", "GraphLiteracy_3",
              "Numeracy_2", "Bayesianreasoning_1", "Certainty_%", "Uncertainty_%", "RiskComprehension_%", "GraphLiteracy_%", "Numeracy_%", "Bayesianreasoning_%",
              "TotalScore_18", "TotalScore_%",]
    
    survey1 = survey[colReq].copy()
    return survey1

d01 = scoring2(d0)
d01

d1 = pd.read_csv('Indian Risk Survey_Besample_Filtered_12_2024.csv')
d11 = d1.iloc[:, 19:62].copy()
d11.insert(0, "ResponseId",0)
d11["ResponseId"] = d1["ResponseId"].copy()
#d11 = d11.loc[ (d11["Q4"] != "Student")]

d11["Q4"].value_counts()

Q4
Salaried/Employee/Consultant in a sector other than Finance    167
Student                                                        100
Not employed                                                    50
Entrepreneur/Business Owner in a sector other than Finance      30
Stock analyst                                                   12
Insurance agent, etc                                             7
Retired                                                          3
Venture capital/private equity                                   1
Name: count, dtype: int64

d11["age"] = d11["Q2"]

# Age groups/rec = [2,3,4]
# Age group 2 = 18 to 35 y/o
# Age group 3 = 36 to 55 y/o
# Age group 4 = 56 to 75 y/o (75 y/o, i.e, within the scope of the data we have, it can mean 60 and above also)

d11["age_rec"] = np.where( (d11["age"] >= 18) & (d11["age"] <= 35), 2,
                          np.where( (d11["age"] >= 36) & (d11["age"] <= 55), 3,
                                   np.where( (d11["age"] >= 56), 4, 0
                                           )
                                  )
                         )


# Secondary upto 10 – ISCED 3
# Senior Secondary upto 12 - ISCED 3 
# Diploma and voca – ISCED 2
# UG – ISCED 2
# PG – ISCED 1
# PhD and higher – ISCED 1

d11["isced"] = np.where(d11["Q3"] == "Post-Graduate Program", 1,
                        np.where(d11["Q3"] == "Undergraduate Program", 2,
                                 np.where(d11["Q3"] == "Ph.D. and higher", 1,
                                          np.where(d11["Q3"] == "Diploma and Vocational Training", 2,
                                                   np.where(d11["Q3"] == "Secondary School (11th to 12th Std.)", 3,
                                                            np.where( d11["Q3"] == "Primary School (up to 10th Std.)", 3,
                                                                     np.where( d11["Q3"] == "M.Phil.", 1, 0
                                                                             )
                                                                    )
                                                           )
                                                  )
                                         )
                                )
                       )

d11["income"] = np.where(d11["Q15a"] == "< INR 500,000", 1,
                         np.where(d11["Q15a"] == "INR 500,001 – INR 15,00,000", 2,
                                  np.where(d11["Q15a"] == "INR 1500,001 – INR 30,00,000", 3,
                                           np.where(d11["Q15a"] == "INR 30,00,001 – INR 50,00,000", 4,
                                                    np.where(d11["Q15a"] == "INR 50,00,001 – INR 75,00,000", 5,
                                                             np.where( d11["Q15a"] == "> INR 75,00,000", 5,0
                                                                     )
                                                            )
                                                   )
                                           )
                                  )
                         )

d11["wealth"] = d11["Q14b.1"]

matchCol = ["q8_2_1", "q8_2_2", "q8_2_3", "q8_2_4", "q8_2_5", "q8_3", "q8_4", "q8_5berlin_1", 
            "q8_5london_1", "q8_5paris_1", "q8_6", "q8_7", "q9_1_1", "q9_2_1", "q9_3",
            "q10_1_1", "q10_2_1", "q10_3_1", "q10_4"]
d11[ matchCol ] = 0

d11.columns

Index(['ResponseId', 'Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5_1', 'Q6_1', 'Q8_1',
       'Q8_2', 'Q8_3', 'Q8_4', 'Q8_5', 'Q8_6', 'Q8_7', 'Q8_8', 'Q9b_1',
       'Q9b_4', 'Q9b_5', 'Q9b_6', 'Q9b_7', 'Q9b_8', 'Q9b_9', 'Q11a_1',
       'Q11a_2', 'Q11a_3', 'Q11b', 'Q11c', 'Q11d', 'Q11h', 'Attention Check',
       'Q11i', 'Q12a', 'Q12b', 'Q12c', 'Q13a', 'Q13b', 'Q13c', 'Q13d', 'Q14a',
       'Q14b', 'Q14c', 'Q15a', 'Q14b.1', 'age', 'age_rec', 'isced', 'income',
       'wealth', 'q8_2_1', 'q8_2_2', 'q8_2_3', 'q8_2_4', 'q8_2_5', 'q8_3',
       'q8_4', 'q8_5berlin_1', 'q8_5london_1', 'q8_5paris_1', 'q8_6', 'q8_7',
       'q9_1_1', 'q9_2_1', 'q9_3', 'q10_1_1', 'q10_2_1', 'q10_3_1', 'q10_4'],
      dtype='object')

d11["q8_2_1"] = np.where(d11["Q11b"].str.contains("HIV test"), 1, 2)
d11["q8_2_2"] = np.where(d11["Q11b"].str.contains("Fingerprint"), 1, 2)
d11["q8_2_3"] = np.where(d11["Q11b"].str.contains("DNA test"), 1, 2)
d11["q8_2_4"] = np.where(d11["Q11b"].str.contains("Cancer screening test"), 1, 2)
d11["q8_2_5"] = np.where(d11["Q11b"].str.contains("Professional horoscope"), 1, 2)

d11["q8_3"] = np.where(d11["Q11c"] == "The lower the quality of the study, the more likely that future studies will change the risk estimate.", 1,
                          np.where(d11["Q11c"] == "The higher the quality of the study, the more likely that future studies will change the risk estimate.", 2,
                                  np.where(d11["Q11c"] == "Irrespective of the quality of the study, future studies will not change the risk estimate.", 3,
                                          np.where(d11["Q11c"] == "Irrespective of the quality of the study, future studies will change the risk estimate substantially anyway.", 4,0
                                                  )
                                          )
                                  )
                         )


d11["q8_4"] = np.where(d11["Q11d"] == "The growth rate will be 0.4% on average each year", 1,
                          np.where(d11["Q11d"] == "The growth rate over five years will be exactly 2%", 2,
                                  np.where(d11["Q11d"] == "The growth rate over five years will be between 1% and 3%", 3,
                                          np.where(d11["Q11d"] == "It is not possible to predict the growth rate with certainty", 4,0
                                                  )
                                          )
                                  )
                         )

d11["q8_5berlin_1"] = np.where(d11["Q11a_1"] == 1, 1, 2)
d11["q8_5london_1"] = np.where(d11["Q11a_1"] == 1, 1, 2)
d11["q8_5paris_1"] = np.where(d11["Q11a_1"] == 1, 1, 2)

d11["q8_6"] = np.where(d11["Q11i"] == "The medication increases recovery by 100%", 1,
                          np.where(d11["Q11i"] == "The medication increases recovery by 50%", 2,
                                   np.where(d11["Q11i"] == "The medication increases recovery by 2%", 3,
                                            np.where(d11["Q11i"] == "None of the above is implied", 4, 0
                                                    )
                                           )
                                  )
                         )

d11["q8_7"] = np.where(d11["Q11h"] == "5 in 100 people prior to the intervention to 6 out of 100 people after the intervention", 1,
                          np.where(d11["Q11h"] == "100 in 10,000 people prior to the intervention to 120 out of 10,000 people after the intervention", 2,
                                   np.where(d11["Q11h"] == "70 in 100 people prior to the intervention to 90 out of 100 people after the intervention", 3,
                                            np.where(d11["Q11h"] == "It is not possible to determine which of the answers is correct given the information provided", 4, 0
                                                    )
                                           )
                                  )
                         )


d11["q9_1_1"] = d11["Q14a"].copy()
d11["q9_2_1"] = d11["Q14b"].copy()

d11["q9_3"] = np.where(d11["Q14c"] == "Crosicol", 1,
                          np.where(d11["Q14c"] == "Hertinol", 2,
                                   np.where(d11["Q14c"] == "They are equal", 3,
                                            np.where(d11["Q14c"] == "Can’t say", 4, 0
                                                    )
                                           )
                                  )
                         )

d11["q10_1_1"] = d11["Q13a"].copy()
d11["q10_2_1"] = d11["Q13b"].copy()
d11["q10_3_1"] = d11["Q13c"].copy()

d11["q10_4"] = np.where(d11["Q13d"] == "9 out of 59", 1,
                          np.where(d11["Q13d"] == "9 out of 10", 2,
                                   np.where(d11["Q13d"] == "59 out of 1000", 3,
                                            np.where(d11["Q13d"] == "59 out of 100", 4, 0
                                                    )
                                           )
                                  )
                         )

d11[ matchCol ]

scoreColumns = ["certainty1", "certainty2", "certainty3", "certainty4", "certainty5", "uncertainty1", "uncertainty2", "numeracy1", "numeracy2", "numeracy3", "numeracy4", "numeracy5", "graph1", "graph2", "graph3", "riskcomprehension1", "riskcomprehension2", "riskcomprehension3", "riskcomprehension4", "riskcomprehension5", "bayesian1"]
d11[scoreColumns] = 0

d11Facet = d11.groupby(["ResponseId"]).progress_apply(scoring1)

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d11Facet

d11 = d11Facet.copy().reset_index(drop = True)

d12 = scoring2(d11)
d12

df2 = pd.concat([d01, d12], axis = 0).reset_index(drop = True)

df2

# The following graph represents frequency of each data point on "TotalScore_18" or the total score out of 13 questions across the sample.

df2['TotalScore_18'].plot(kind = 'hist', xticks = np.arange(0, 20, step=1), xlabel = 'TotalScore_18', title = 'Frequency of Scores' )

<Axes: title={'center': 'Frequency of Scores'}, xlabel='TotalScore_18', ylabel='Frequency'>

# Absolute mean scores for each facet

(df2[['Certainty_5', 'Uncertainty_2', 'RiskComprehension_5','GraphLiteracy_3','Numeracy_2','Bayesianreasoning_1','TotalScore_18']].mean(axis = 0)).plot(kind = 'bar', title = 'Absolute mean of scores for above data set')

<Axes: title={'center': 'Absolute mean of scores for above data set'}>

# Normalised mean scores for each facet

df2[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%','TotalScore_%']].mean(axis = 0).plot(kind = 'bar', title = '% mean of scores for above data set')

<Axes: title={'center': '% mean of scores for above data set'}>

# Data Frame 5 or df5 is an aggregate data on facet total scores and over all total score, along with  education data vs each response. 
# This data set is now sorted by education.

df5 = df2.sort_values(by = 'isced')
df5 = df5.reset_index(drop = True)

df5

# Mean of each facet as a % for each category of isced

df5.groupby('isced')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%']].mean()

# Count of responses for each category isced

df5.groupby('isced')[['ResponseId']].count()

# Absolute Total Facet scores mean line plot from low to high ISCED

df5.groupby('isced')[['TotalScore_18']].mean().plot( kind = 'line', title = 'Absolute Total Facet scores mean line plot', xticks = np.arange(1,4, step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x23743390810>

# Absolute Facet scores mean line plot from low to high edu

df5.groupby('isced')[['Certainty_5', "Uncertainty_2", 'RiskComprehension_5','Numeracy_2','GraphLiteracy_3','Bayesianreasoning_1']].mean().plot( kind = 'line', title = 'Absolute Facet scores mean line plot', xticks = np.arange(1,4, step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x23742eaa3d0>

# Normalised Facet scores mean line plot from low to high edu


df5.groupby('isced')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean().plot( kind = 'line', title = 'Normalised Facet scores mean line plot', xticks = np.arange(1,4, step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x23741fe13d0>

# Normalised mean scores for each facet stacked

df5.groupby('isced')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%']].mean().plot( kind = 'bar', title = 'Normalised mean scores for each facet stacked', stacked = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x237431c0910>

# Normalised mean scores for each facet for each edu response category

df5.groupby('isced')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean().T.plot(kind = 'bar', title = 'Normalised mean scores for each facet for each edu response category').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x237437cea10>

# Trend line for Absolute Total Facet Score vs edu (isced) reponses

sns.regplot (data = df5, x = 'isced', y = 'TotalScore_18')

<Axes: xlabel='isced', ylabel='TotalScore_18'>

# Trend line for Absolute Independent Facet Score vs edu (isced) reponses

fig, ax6 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df5, x = 'isced', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax6, label='Certainty_5')
sns.regplot (data = df5, x = 'isced', y = 'Uncertainty_2', fit_reg=True, ci=None, ax=ax6, label='Uncertainty_2')
sns.regplot (data = df5, x = 'isced', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax6, label='RiskComprehension_5')
sns.regplot (data = df5, x = 'isced', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax6, label='Numeracy_2')
sns.regplot (data = df5, x = 'isced', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax6, label='GraphLiteracy_3')
sns.regplot (data = df5, x = 'isced', y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax6, label='Bayesianreasoning_1' )

ax6.set(ylabel='Scores', xlabel='isced')
ax6.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Violine Plot for TotalScore_18 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'TotalScore_18')

<Axes: xlabel='isced', ylabel='TotalScore_18'>

# Violine Plot for Certainty_5 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Certainty_5')

<Axes: xlabel='isced', ylabel='Certainty_5'>

# Violine Plot for Uncertainty_5 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Uncertainty_2')

<Axes: xlabel='isced', ylabel='Uncertainty_2'>

# Violine Plot for RiskComprehension_5 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'RiskComprehension_5')

<Axes: xlabel='isced', ylabel='RiskComprehension_5'>

# Violine Plot for GraphLiteracy_3 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'GraphLiteracy_3')

<Axes: xlabel='isced', ylabel='GraphLiteracy_3'>

# Violine Plot for Numeracy_2 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Numeracy_2')

<Axes: xlabel='isced', ylabel='Numeracy_2'>

# Violine Plot for TotalScore_18 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Bayesianreasoning_1')

<Axes: xlabel='isced', ylabel='Bayesianreasoning_1'>

# DF3 = Sorted by income

df3 = df2.sort_values(by = 'income')
df3 = df3.reset_index(drop = True)
df3.drop(df3[df3['income'] == 7].index, inplace = True)

df3

# Normalised mean of each facet as a numerical (sorted by income)

df3.groupby('income')[['Certainty_%','RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%','TotalScore_%']].mean()

# Count of responses for each category (sorted by income)

df3.groupby('income')[['ResponseId']].count()

# Absolute mean of Total Facet score line plot (sorted by income)

df3.groupby('income')[['TotalScore_18']].mean().plot( kind = 'line', title = 'Absolute mean of Total Facet score line plot (sorted by income)').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x23743779dd0>

# Absolute mean of each Facet score line plot (sorted by income)

df3.groupby('income')[['Certainty_5', "Uncertainty_2", 'RiskComprehension_5','GraphLiteracy_3','Numeracy_2','Bayesianreasoning_1']].mean().plot( kind = 'line', title = 'Absolute Facet scores mean line plot').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x23741b49190>

# Normalised mean of each Facet score line plot (sorted by income)


df3.groupby('income')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%', 'TotalScore_%']].mean().plot( kind = 'line', title = '% mean of each Facet score line plot (sorted by income)').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x23742e7a2d0>

# Normalised mean of each Facet score stacked plot (sorted by income)

df3.groupby('income')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%']].mean().plot( kind = 'bar', title = '% mean of each Facet score stacked plot (sorted by income)', stacked = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x23741783fd0>

# Normalised mean of each Facet score hist plot (sorted by income)

df3.groupby('income')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%','TotalScore_%']].mean().T.plot(kind = 'bar', title = '% mean scores for each facet for each income response category').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x23742e70350>

# Trend line for Absolute Total Facet Score vs income

sns.regplot (data = df3, x = 'income', y = 'TotalScore_18')

<Axes: xlabel='income', ylabel='TotalScore_18'>

# Trend line for Absolute Independent Facet Score vs income reponses

fig, ax = plt.subplots(figsize=(6, 6))

sns.regplot (data = df3, x = 'income', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax, label='Certainty_5')
sns.regplot (data = df3, x = 'income', y = 'Uncertainty_2', fit_reg=True, ci=None, ax=ax, label='Uncertainty_2')
sns.regplot (data = df3, x = 'income', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax, label='RiskComprehension_5')
sns.regplot (data = df3, x = 'income', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax, label='Numeracy_2')
sns.regplot (data = df3, x = 'income', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax, label='GraphLiteracy_3')
sns.regplot (data = df3, x = 'income', y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax, label='Bayesianreasoning_1' )

ax.set(ylabel='Scores', xlabel='income')
ax.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Violin Plot for TotalScore_18 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'TotalScore_18')

<Axes: xlabel='income', ylabel='TotalScore_18'>

# Violin Plot for Certainty_5 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'Certainty_5')

<Axes: xlabel='income', ylabel='Certainty_5'>

# Violin Plot for Certainty_5 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'Uncertainty_2')

<Axes: xlabel='income', ylabel='Uncertainty_2'>

# Violine Plot for RiskComprehension_5 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'RiskComprehension_5')

<Axes: xlabel='income', ylabel='RiskComprehension_5'>

# Violine Plot for GraphLiteracy_3 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'GraphLiteracy_3')

<Axes: xlabel='income', ylabel='GraphLiteracy_3'>

# Violine Plot for Numeracy_2 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'Numeracy_2')

<Axes: xlabel='income', ylabel='Numeracy_2'>

# Violine Plot for TotalScore_18 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'Bayesianreasoning_1')

<Axes: xlabel='income', ylabel='Bayesianreasoning_1'>

# Descriptive stats for the data set, isced = 1
# NA values of income are removed

df7 = df5
df7.drop(df7[df7['income'] == 7].index, inplace = True)

df7.loc[df7['isced']==1].describe()

df7.loc[df7['isced']==2].describe()

df7.loc[df7['isced']==3].describe()

# Trend line for Absolute Tota Facet Score vs income reponses sorted by isced and ORDERED by income WITH scatter

fig, ax7 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = df7.loc[df7['isced']==1]['TotalScore_18'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 1')
sns.regplot (data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = df7.loc[df7['isced']==2]['TotalScore_18'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 2')
sns.regplot (data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = df7.loc[df7['isced']==3]['TotalScore_18'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 3')

ax7.set(ylabel='Total Scores_19', xlabel='INCOME')
ax7.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Tota Facet Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax8 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 3')

ax8.set(ylabel='Total Scores_19', xlabel='INCOME')
ax8.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Certainty Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax9 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 3')

ax9.set(ylabel='Certainty_5', xlabel='INCOME')
ax9.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Risk Comprehension Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax10 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 3')

ax10.set(ylabel='RiskComprehension_5', xlabel='INCOME')
ax10.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Number Comprehension Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='Numeracy_2', xlabel='INCOME')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Graph Comprehension Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='GraphLiteracy_3', xlabel='INCOME')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Bayesian Reasoning Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax12 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 3')

ax12.set(ylabel='Bayesianreasoning_1', xlabel='INCOME')
ax12.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

df7

# Since we already have a classification for Age groups in the form of age_rec, we will use that.
# We will also use median of age to see if it yields any relevant results, as instructed.

# AXES to be used = Age or age groups, ISCED, Income

# Age groups = [2,3,4]
# Age group 2 = 18 to 35 y/o
# Age group 3 = 36 to 59 y/o
# Age group 4 = 60 to 75 y/o (75 y/o, i.e, within the scope of the data we have, it can mean 60 and above also)

df7.loc[(df7['age_rec']==2)].describe()

df7.loc[(df7['age_rec']==3)].describe()

df7.loc[(df7['age_rec']==4)].describe()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Total Scores_14', xlabel='INCOME for age 18 to 35')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_14', xlabel='INCOME for age 36 to 55')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Total Scores_14', xlabel='INCOME for age 56 and above')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ df7['age_rec']==2  ]["income"].unique(), y = df7.loc[ df7['age_rec']==2  ].groupby(['income']).mean(numeric_only=True)['TotalScore_18'], yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Total Scores_14', xlabel='INCOME for age 18 to 35',  yticks = np.arange(5, 16 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_14', xlabel='INCOME for age 36 to 55',  yticks = np.arange(5, 16 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df7.loc[ df7['age_rec']==3  ]["income"].unique(), y = df7.loc[ df7['age_rec']==3  ].groupby(['income']).mean(numeric_only=True)['TotalScore_18'], yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Total Scores_14', xlabel='INCOME for age 56 and above',  yticks = np.arange(5, 16 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income']).mean(numeric_only=True)['TotalScore_18'], yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Certainty_5', xlabel='INCOME for age 18 to 35',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df7.loc[ df7['age_rec']==2  ]["income"].unique(), y = df7.loc[ df7['age_rec']==2  ].groupby(['income'])['Certainty_5'].mean(), yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Certainty_5', xlabel='INCOME for age 36 to 55',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df7.loc[ df7['age_rec']==3  ]["income"].unique(), y = df7.loc[ df7['age_rec']==3  ].groupby(['income'])['Certainty_5'].mean(), yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Certainty_5', xlabel='INCOME for age 56 and above',  yticks = np.arange(0, 6 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income'])['Certainty_5'].mean(), yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='RiskComprehension_5', xlabel='INCOME for age 18 to 35',  yticks = np.arange(0, 3 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df7.loc[ df7['age_rec']==2  ]["income"].unique(), y = df7.loc[ df7['age_rec']==2  ].groupby(['income'])['RiskComprehension_5'].mean(), yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='RiskComprehension_5', xlabel='INCOME for age 36 to 55',  yticks = np.arange(0, 3 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df7.loc[ df7['age_rec']==3  ]["income"].unique(), y = df7.loc[ df7['age_rec']==3  ].groupby(['income'])['RiskComprehension_5'].mean(), yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='RiskComprehension_5', xlabel='INCOME for age 56 and above',  yticks = np.arange(0, 3 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income'])['RiskComprehension_5'].mean(), yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Numeracy_2', xlabel='INCOME for age 18 to 35',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df7.loc[ df7['age_rec']==2  ]["income"].unique(), y = df7.loc[ df7['age_rec']==2  ].groupby(['income'])['Numeracy_2'].mean(), yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Numeracy_2', xlabel='INCOME for age 36 to 55',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df7.loc[ df7['age_rec']==3  ]["income"].unique(), y = df7.loc[ df7['age_rec']==3  ].groupby(['income'])['Numeracy_2'].mean(), yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Numeracy_2', xlabel='INCOME for age 56 and above',  yticks = np.arange(0, 6 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income'])['Numeracy_2'].mean(), yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='GraphLiteracy_3', xlabel='INCOME for age 18 to 35',  yticks = np.arange(0, 4 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df7.loc[ df7['age_rec']==2  ]["income"].unique(), y = df7.loc[ df7['age_rec']==2  ].groupby(['income'])['GraphLiteracy_3'].mean(), yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='GraphLiteracy_3', xlabel='INCOME for age 36 to 55',  yticks = np.arange(0, 4 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df7.loc[ df7['age_rec']==3  ]["income"].unique(), y = df7.loc[ df7['age_rec']==3  ].groupby(['income'])['GraphLiteracy_3'].mean(), yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='GraphLiteracy_3', xlabel='INCOME for age 56 and above',  yticks = np.arange(0, 4 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income'])['GraphLiteracy_3'].mean(), yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Bayesianreasoning_1', xlabel='INCOME for age 18 to 35',  yticks = np.arange(0, 5 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df7.loc[ df7['age_rec']==2  ]["income"].unique(), y = df7.loc[ df7['age_rec']==2  ].groupby(['income'])['Bayesianreasoning_1'].mean(), yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Bayesianreasoning_1', xlabel='INCOME for age 36 to ',  yticks = np.arange(0, 5 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df7.loc[ df7['age_rec']==3  ]["income"].unique(), y = df7.loc[ df7['age_rec']==3  ].groupby(['income'])['Bayesianreasoning_1'].mean(), yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Bayesianreasoning_1', xlabel='INCOME for age 56 and above',  yticks = np.arange(0, 5 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income'])['Bayesianreasoning_1'].mean(), yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

# Age mean method
# Age_mean

age_m = df2["age"].median()
age_m

30.5

df7["income"].value_counts()

income
2    387
1    324
3    156
4     97
5     49
Name: count, dtype: int64

df7.loc[(df7['age'] < age_m)].describe()

df7.loc[(df7['age'] >= age_m)].describe()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Total Scores_19', xlabel='INCOME for < median age')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age'] >= age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_19', xlabel='INCOME for >=median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ (df7['age'] < age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['TotalScore_18'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Total Scores_19', xlabel='INCOME for < median age',  yticks = np.arange(5, 16 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df7.loc[ (df7['age'] >= age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] >=age_m)  ].groupby(['income'])['TotalScore_18'].mean(), yerr = df7.loc[ (df7['age'] >=age_m) ].groupby(['income'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Total Scores_19', xlabel='INCOME for >=median age',  yticks = np.arange(5, 16 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ (df7['age'] < age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['Certainty_5'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Certainty_5', xlabel='INCOME for < median age',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df7.loc[ (df7['age'] >= age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] >=age_m)  ].groupby(['income'])['Certainty_5'].mean(), yerr = df7.loc[ (df7['age'] >=age_m) ].groupby(['income'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Certainty_5', xlabel='INCOME for >=median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ (df7['age'] < age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['RiskComprehension_5'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='RiskComprehension_5', xlabel='INCOME for < median age',  yticks = np.arange(0, 3 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df7.loc[ (df7['age'] >= age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] >=age_m)  ].groupby(['income'])['RiskComprehension_5'].mean(), yerr = df7.loc[ (df7['age'] >=age_m) ].groupby(['income'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='RiskComprehension_5', xlabel='INCOME for >=median age',  yticks = np.arange(0, 3 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ (df7['age'] < age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['Numeracy_2'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Numeracy_2', xlabel='INCOME for < median age',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df7.loc[ (df7['age'] >= age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] >=age_m)  ].groupby(['income'])['Numeracy_2'].mean(), yerr = df7.loc[ (df7['age'] >=age_m) ].groupby(['income'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Numeracy_2', xlabel='INCOME for >=median age',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ (df7['age'] < age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['GraphLiteracy_3'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='GraphLiteracy_3', xlabel='INCOME for < median age',  yticks = np.arange(0, 4 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df7.loc[ (df7['age'] >= age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] >=age_m)  ].groupby(['income'])['GraphLiteracy_3'].mean(), yerr = df7.loc[ (df7['age'] >=age_m) ].groupby(['income'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='GraphLiteracy_3', xlabel='INCOME for >=median age',  yticks = np.arange(0, 4 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ (df7['age'] < age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['Bayesianreasoning_1'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Bayesianreasoning_1', xlabel='INCOME for < median age',  yticks = np.arange(0, 5 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df7.loc[ (df7['age'] >= age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] >=age_m)  ].groupby(['income'])['Bayesianreasoning_1'].mean(), yerr = df7.loc[ (df7['age'] >=age_m) ].groupby(['income'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Bayesianreasoning_1', xlabel='INCOME for >=median age',  yticks = np.arange(0, 5 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Data Frame 2 or df2 is an aggregate data on facet total scores and over all total score, along with wealth and income data vs each response. 
# This data set is also sorted by wealth with all responses 'NA' removed.
# We also assign quartiles ranking each response according to this sort.

df4 = df2.sort_values(by = 'wealth')
df4 = df4.reset_index(drop = True)
df4 = df4.dropna(axis = 0, subset = 'wealth')
df4.insert(loc = len(df4.columns), column = "Quartile Number", value = pd.qcut(df4["wealth"],q = 4, labels = False ) + 1, allow_duplicates = 'False')

df4

# Trend line for Absolute Total Facet Score vs unique wealth reponses

sns.regplot (data = df4, x = df4.index, y = 'TotalScore_18')

<Axes: ylabel='TotalScore_18'>

# Trend line for Absolute Independent Facet Score vs wealth reponses

fig1, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4, x = df4.index, y = 'Certainty_5', fit_reg=True, ci=None, ax=ax1, label='Certainty_5')
sns.regplot (data = df4, x = df4.index, y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax1, label='RiskComprehension_5')
sns.regplot (data = df4, x = df4.index, y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax1, label='Numeracy_2')
sns.regplot (data = df4, x = df4.index, y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax1, label='GraphLiteracy_3')
sns.regplot (data = df4, x = df4.index, y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax1, label='Bayesianreasoning_1' )

ax1.set(ylabel='Scores', xlabel='wealth')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Count of number of responses under each quartile

df4.groupby('Quartile Number')[['Quartile Number']].count()

# Absolute Total Facet scores mean line plot from low to high Wealth

df4.groupby('Quartile Number')[['TotalScore_18']].mean().plot( kind = 'line', title = 'Absolute Total Facet scores mean line plot low to high wealth', xticks = np.arange(1,5,step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x23742de0210>

# Absolute Facet scores mean line plot from low to high wealth

df4.groupby('Quartile Number')[['Certainty_5', "Uncertainty_2", 'RiskComprehension_5','Numeracy_2','GraphLiteracy_3','Bayesianreasoning_1']].mean().plot( kind = 'line', title = 'Absolute Facet scores mean line plot low to high wealth', xticks = np.arange(1,5,step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x2374367cc90>

# Normalised Facet scores mean line plot from low to high wealth


df4.groupby('Quartile Number')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean().plot( kind = 'line', title = '% Facet scores mean line plot from low to high wealth',  xticks = np.arange(1,5,step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x237435f1490>

# Normalised mean scores for each facet stacked

df4.groupby('Quartile Number')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%']].mean().plot( kind = 'bar', title = '% mean scores for each facet stacked', stacked = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x2374368cfd0>

# Normalised mean scores for each facet for each wealth response Quartile

df4.groupby('Quartile Number')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean().T.plot(kind = 'bar', title = '% mean scores for each facet for each wealth response category').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x2374154cf10>

# Trend line for Absolute Total Facet Score vs Quartiles

sns.regplot (data = df4, x = 'Quartile Number', y = 'TotalScore_18')

<Axes: xlabel='Quartile Number', ylabel='TotalScore_18'>

# Trend line for Absolute Independent Facet Score vs Quartiles

fig2, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4, x = 'Quartile Number', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax2, label='Certainty_5')
sns.regplot (data = df4, x = 'Quartile Number', y = 'Uncertainty_2', fit_reg=True, ci=None, ax=ax2, label='Uncertainty_2')
sns.regplot (data = df4, x = 'Quartile Number', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax2, label='RiskComprehension_5')
sns.regplot (data = df4, x = 'Quartile Number', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax2, label='Numeracy_2')
sns.regplot (data = df4, x = 'Quartile Number', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax2, label='GraphLiteracy_3')
sns.regplot (data = df4, x = 'Quartile Number', y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax2, label='Bayesianreasoning_1' )

ax2.set(ylabel='Scores', xlabel='wealth')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Violine Plot for TotalScore_18 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'TotalScore_18')

<Axes: xlabel='Quartile Number', ylabel='TotalScore_18'>

# Violine Plot for Certainty_5 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Certainty_5')

<Axes: xlabel='Quartile Number', ylabel='Certainty_5'>

# Violine Plot for Certainty_5 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Uncertainty_2')

<Axes: xlabel='Quartile Number', ylabel='Uncertainty_2'>

# Violine Plot for RiskComprehension_5 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'RiskComprehension_5')

<Axes: xlabel='Quartile Number', ylabel='RiskComprehension_5'>

# Violine Plot for GraphLiteracy_3 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'GraphLiteracy_3')

<Axes: xlabel='Quartile Number', ylabel='GraphLiteracy_3'>

# Violine Plot for Numeracy_2 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Numeracy_2')

<Axes: xlabel='Quartile Number', ylabel='Numeracy_2'>

# Violine Plot for Bayesianreasoning_1 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Bayesianreasoning_1')

<Axes: xlabel='Quartile Number', ylabel='Bayesianreasoning_1'>

# Descriptive stats for the data set, isced = 1
# NA values of wealth are removed

df6 = df5.dropna(axis = 0, subset = 'wealth')
# df6.drop(df6[df6['wealth'] == 3500000].index, inplace = True)
df6.loc[df6['isced']==1][['wealth']].describe()

# Descriptive stats for the data set, isced = 2

df6.loc[df6['isced']==2][['wealth']].describe()

# Descriptive stats for the data set, isced = 3

df6.loc[df6['isced']==3][['wealth']].describe()

# Trend line for Absolute Tota Facet Score vs wealth reponses sorted by isced and ORDERED by wealth WITH scatter

fig, ax7 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax7, label='ISCED = 1')
sns.regplot (data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax7, label='ISCED = 2')
sns.regplot (data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax7, label='ISCED = 3')

ax7.set(ylabel='Total Scores_19', xlabel='Wealth')
ax7.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Tota Facet Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax8 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 3')

ax8.set(ylabel='Total Scores_19', xlabel='Wealth')
ax8.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Certainty Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax9 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 3')

ax9.set(ylabel='Certainty_5', xlabel='Wealth')
ax9.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Uncertainty Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax10 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 3')

ax10.set(ylabel='RiskComprehension_5', xlabel='Wealth')
ax10.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Number Comprehension Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='Numeracy_2', xlabel='Wealth')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Graph Comprehension Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='GraphLiteracy_3', xlabel='Wealth')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Bayesian Reasoning Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax12 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 3')

ax12.set(ylabel='Bayesianreasoning_1', xlabel='Wealth')
ax12.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Descriptive stats for the data set, isced = 1
# NA values of wealth are removed

df4.loc[df4['isced']==1].describe()

df4.loc[df4['isced']==2].describe()

df4.loc[df4['isced']==3].describe()

# Trend line for Absolute Tota Facet Score vs Quartile Number reponses sorted by isced and ORDERED by Quartile Number WITH scatter

fig, ax7 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[df4['isced']==1].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[df4['isced']==1]['TotalScore_18'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 1')
sns.regplot (data = df4.loc[df4['isced']==2].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[df4['isced']==2]['TotalScore_18'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 2')
sns.regplot (data = df4.loc[df4['isced']==3].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[df4['isced']==3]['TotalScore_18'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 3')

ax7.set(ylabel='Total Scores_19', xlabel='WEALTH Quartile Number')
ax7.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Tota Facet Score vs Quartile Number reponses sorted by isced and ORDERED by Quartile Number WITHOUT scatter

fig, ax8 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[df4['isced']==1].reset_index(drop = True), x = 'Quartile Number', y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==2].reset_index(drop = True), x = 'Quartile Number', y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==3].reset_index(drop = True), x = 'Quartile Number', y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 3')

ax8.set(ylabel='Total Scores_19', xlabel='WEALTH Quartile Number')
ax8.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Certainty Score vs Quartile Number reponses sorted by isced and ORDERED by Quartile Number WITHOUT scatter

fig, ax9 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[df4['isced']==1].reset_index(drop = True), x = 'Quartile Number', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==2].reset_index(drop = True), x = 'Quartile Number', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==3].reset_index(drop = True), x = 'Quartile Number', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 3')

ax9.set(ylabel='Certainty_5', xlabel='WEALTH Quartile Number')
ax9.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Risk Comprehension Score vs Quartile Number reponses sorted by isced and ORDERED by Quartile Number WITHOUT scatter

fig, ax10 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[df4['isced']==1].reset_index(drop = True), x = 'Quartile Number', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==2].reset_index(drop = True), x = 'Quartile Number', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==3].reset_index(drop = True), x = 'Quartile Number', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 3')

ax10.set(ylabel='RiskComprehension_5', xlabel='WEALTH Quartile Number')
ax10.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Number Comprehension Score vs Quartile Number reponses sorted by isced and ORDERED by Quartile Number WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[df4['isced']==1].reset_index(drop = True), x = 'Quartile Number', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==2].reset_index(drop = True), x = 'Quartile Number', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==3].reset_index(drop = True), x = 'Quartile Number', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='Numeracy_2', xlabel='WEALTH Quartile Number')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Graph Comprehension Score vs Quartile Number reponses sorted by isced and ORDERED by Quartile Number WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[df4['isced']==1].reset_index(drop = True), x = 'Quartile Number', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==2].reset_index(drop = True), x = 'Quartile Number', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==3].reset_index(drop = True), x = 'Quartile Number', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='GraphLiteracy_3', xlabel='WEALTH Quartile Number')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Bayesian Reasoning Score vs Quartile Number reponses sorted by isced and ORDERED by Quartile Number WITHOUT scatter

fig, ax12 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[df4['isced']==1].reset_index(drop = True), x = 'Quartile Number', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==2].reset_index(drop = True), x = 'Quartile Number', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==3].reset_index(drop = True), x = 'Quartile Number', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 3')

ax12.set(ylabel='Bayesianreasoning_1', xlabel='WEALTH Quartile Number')
ax12.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

df4

# Since we already have a classification for Age groups in the form of age_rec, we will use that.
# We will also use median of age to see if it yields any relevant results, as instructed.

# AXES to be used = Age or age groups, ISCED, Income

# Age groups = [2,3,4]
# Age group 2 = 18 to 35 y/o
# Age group 3 = 36 to 59 y/o
# Age group 4 = 60 to 75 y/o (75 y/o, i.e, within the scope of the data we have, it can mean 60 and above also)

df4.loc[(df4['age_rec']==2)].describe()

df4.loc[(df4['age_rec']==3)].describe()

df4.loc[(df4['age_rec']==4)].describe()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (data = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (data = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Total Scores_14', xlabel='WEALTH Quartile Number for age 18 to 35')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (data = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (data = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_14', xlabel='WEALTH Quartile Number for age 36 to 55')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (data = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (data = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Total Scores_14', xlabel='WEALTH Quartile Number for age 56 and above')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df4.loc[ df4['age_rec']==2  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==2  ].groupby(['Quartile Number']).mean(numeric_only=True)['TotalScore_18'], yerr = df4.loc[ (df4['age_rec']==2) ].groupby(['Quartile Number'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Total Scores_14', xlabel='WEALTH Quartile Number for age 18 to 35',  yticks = np.arange(5, 16 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_14', xlabel='WEALTH Quartile Number for age 36 to 55',  yticks = np.arange(5, 16 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df4.loc[ df4['age_rec']==3  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==3  ].groupby(['Quartile Number']).mean(numeric_only=True)['TotalScore_18'], yerr = df4.loc[ (df4['age_rec']==3) ].groupby(['Quartile Number'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Total Scores_14', xlabel='WEALTH Quartile Number for age 56 and above',  yticks = np.arange(5, 16 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df4.loc[ df4['age_rec']== 4 ]['Quartile Number'].unique(), y = df4.loc[ df4['age_rec']==4  ].groupby(['Quartile Number']).mean(numeric_only=True)['TotalScore_18'], yerr = df4.loc[ (df4['age_rec']==4) ].groupby(['Quartile Number'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Certainty_5', xlabel='WEALTH Quartile Number for age 18 to 35',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df4.loc[ df4['age_rec']==2  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==2  ].groupby(['Quartile Number'])['Certainty_5'].mean(), yerr = df4.loc[ (df4['age_rec']==2) ].groupby(['Quartile Number'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Certainty_5', xlabel='WEALTH Quartile Number for age 36 to 55',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df4.loc[ df4['age_rec']==3  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==3  ].groupby(['Quartile Number'])['Certainty_5'].mean(), yerr = df4.loc[ (df4['age_rec']==3) ].groupby(['Quartile Number'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Certainty_5', xlabel='WEALTH Quartile Number for age 56 and above',  yticks = np.arange(0, 6 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df4.loc[ df4['age_rec']== 4 ]['Quartile Number'].unique(), y = df4.loc[ df4['age_rec']==4  ].groupby(['Quartile Number'])['Certainty_5'].mean(), yerr = df4.loc[ (df4['age_rec']==4) ].groupby(['Quartile Number'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='RiskComprehension_5', xlabel='WEALTH Quartile Number for age 18 to 35',  yticks = np.arange(0, 3 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df4.loc[ df4['age_rec']==2  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==2  ].groupby(['Quartile Number'])['RiskComprehension_5'].mean(), yerr = df4.loc[ (df4['age_rec']==2) ].groupby(['Quartile Number'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='RiskComprehension_5', xlabel='WEALTH Quartile Number for age 36 to 55',  yticks = np.arange(0, 3 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df4.loc[ df4['age_rec']==3  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==3  ].groupby(['Quartile Number'])['RiskComprehension_5'].mean(), yerr = df4.loc[ (df4['age_rec']==3) ].groupby(['Quartile Number'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='RiskComprehension_5', xlabel='WEALTH Quartile Number for age 56 and above',  yticks = np.arange(0, 3 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df4.loc[ df4['age_rec']== 4 ]['Quartile Number'].unique(), y = df4.loc[ df4['age_rec']==4  ].groupby(['Quartile Number'])['RiskComprehension_5'].mean(), yerr = df4.loc[ (df4['age_rec']==4) ].groupby(['Quartile Number'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Numeracy_2', xlabel='WEALTH Quartile Number for age 18 to 35',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df4.loc[ df4['age_rec']==2  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==2  ].groupby(['Quartile Number'])['Numeracy_2'].mean(), yerr = df4.loc[ (df4['age_rec']==2) ].groupby(['Quartile Number'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Numeracy_2', xlabel='WEALTH Quartile Number for age 36 to 55',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df4.loc[ df4['age_rec']==3  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==3  ].groupby(['Quartile Number'])['Numeracy_2'].mean(), yerr = df4.loc[ (df4['age_rec']==3) ].groupby(['Quartile Number'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Numeracy_2', xlabel='WEALTH Quartile Number for age 56 and above',  yticks = np.arange(0, 6 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df4.loc[ df4['age_rec']== 4 ]['Quartile Number'].unique(), y = df4.loc[ df4['age_rec']==4  ].groupby(['Quartile Number'])['Numeracy_2'].mean(), yerr = df4.loc[ (df4['age_rec']==4) ].groupby(['Quartile Number'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='GraphLiteracy_3', xlabel='WEALTH Quartile Number for age 18 to 35',  yticks = np.arange(0, 4 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df4.loc[ df4['age_rec']==2  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==2  ].groupby(['Quartile Number'])['GraphLiteracy_3'].mean(), yerr = df4.loc[ (df4['age_rec']==2) ].groupby(['Quartile Number'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='GraphLiteracy_3', xlabel='WEALTH Quartile Number for age 36 to 55',  yticks = np.arange(0, 4 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df4.loc[ df4['age_rec']==3  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==3  ].groupby(['Quartile Number'])['GraphLiteracy_3'].mean(), yerr = df4.loc[ (df4['age_rec']==3) ].groupby(['Quartile Number'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='GraphLiteracy_3', xlabel='WEALTH Quartile Number for age 56 and above',  yticks = np.arange(0, 4 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df4.loc[ df4['age_rec']== 4 ]['Quartile Number'].unique(), y = df4.loc[ df4['age_rec']==4  ].groupby(['Quartile Number'])['GraphLiteracy_3'].mean(), yerr = df4.loc[ (df4['age_rec']==4) ].groupby(['Quartile Number'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Bayesianreasoning_1', xlabel='WEALTH Quartile Number for age 18 to 35',  yticks = np.arange(0, 5 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df4.loc[ df4['age_rec']==2  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==2  ].groupby(['Quartile Number'])['Bayesianreasoning_1'].mean(), yerr = df4.loc[ (df4['age_rec']==2) ].groupby(['Quartile Number'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Bayesianreasoning_1', xlabel='WEALTH Quartile Number for age 36 to ',  yticks = np.arange(0, 5 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df4.loc[ df4['age_rec']==3  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==3  ].groupby(['Quartile Number'])['Bayesianreasoning_1'].mean(), yerr = df4.loc[ (df4['age_rec']==3) ].groupby(['Quartile Number'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Bayesianreasoning_1', xlabel='WEALTH Quartile Number for age 56 and above',  yticks = np.arange(0, 5 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df4.loc[ df4['age_rec']== 4 ]['Quartile Number'].unique(), y = df4.loc[ df4['age_rec']==4  ].groupby(['Quartile Number'])['Bayesianreasoning_1'].mean(), yerr = df4.loc[ (df4['age_rec']==4) ].groupby(['Quartile Number'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

# Age mean method
# Age_mean

age_m = df2["age"].median()
age_m

30.5

df4["Quartile Number"].value_counts()

Quartile Number
2    296
1    255
3    242
4    220
Name: count, dtype: int64

df4.loc[(df4['age'] < age_m)].describe()

df4.loc[(df4['age'] >= age_m)].describe()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (data = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (data = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Total Scores_19', xlabel='WEALTH Quartile Number for < median age')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[(df4['age'] >= age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_19', xlabel='WEALTH Quartile Number for >=median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df4.loc[ (df4['age'] < age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] < age_m)  ].groupby(['Quartile Number'])['TotalScore_18'].mean(), yerr = df4.loc[ (df4['age'] < age_m) ].groupby(['Quartile Number'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Total Scores_19', xlabel='WEALTH Quartile Number for < median age',  yticks = np.arange(5, 16 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df4.loc[ (df4['age'] >= age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] >=age_m)  ].groupby(['Quartile Number'])['TotalScore_18'].mean(), yerr = df4.loc[ (df4['age'] >=age_m) ].groupby(['Quartile Number'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Total Scores_19', xlabel='WEALTH Quartile Number for >=median age',  yticks = np.arange(5, 16 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df4.loc[ (df4['age'] < age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] < age_m)  ].groupby(['Quartile Number'])['Certainty_5'].mean(), yerr = df4.loc[ (df4['age'] < age_m) ].groupby(['Quartile Number'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Certainty_5', xlabel='WEALTH Quartile Number for < median age',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df4.loc[ (df4['age'] >= age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] >=age_m)  ].groupby(['Quartile Number'])['Certainty_5'].mean(), yerr = df4.loc[ (df4['age'] >=age_m) ].groupby(['Quartile Number'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Certainty_5', xlabel='WEALTH Quartile Number for >=median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df4.loc[ (df4['age'] < age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] < age_m)  ].groupby(['Quartile Number'])['RiskComprehension_5'].mean(), yerr = df4.loc[ (df4['age'] < age_m) ].groupby(['Quartile Number'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='RiskComprehension_5', xlabel='WEALTH Quartile Number for < median age',  yticks = np.arange(0, 3 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df4.loc[ (df4['age'] >= age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] >=age_m)  ].groupby(['Quartile Number'])['RiskComprehension_5'].mean(), yerr = df4.loc[ (df4['age'] >=age_m) ].groupby(['Quartile Number'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='RiskComprehension_5', xlabel='WEALTH Quartile Number for >=median age',  yticks = np.arange(0, 3 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df4.loc[ (df4['age'] < age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] < age_m)  ].groupby(['Quartile Number'])['Numeracy_2'].mean(), yerr = df4.loc[ (df4['age'] < age_m) ].groupby(['Quartile Number'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Numeracy_2', xlabel='WEALTH Quartile Number for < median age',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df4.loc[ (df4['age'] >= age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] >=age_m)  ].groupby(['Quartile Number'])['Numeracy_2'].mean(), yerr = df4.loc[ (df4['age'] >=age_m) ].groupby(['Quartile Number'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Numeracy_2', xlabel='WEALTH Quartile Number for >=median age',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df4.loc[ (df4['age'] < age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] < age_m)  ].groupby(['Quartile Number'])['GraphLiteracy_3'].mean(), yerr = df4.loc[ (df4['age'] < age_m) ].groupby(['Quartile Number'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='GraphLiteracy_3', xlabel='WEALTH Quartile Number for < median age',  yticks = np.arange(0, 4 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df4.loc[ (df4['age'] >= age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] >=age_m)  ].groupby(['Quartile Number'])['GraphLiteracy_3'].mean(), yerr = df4.loc[ (df4['age'] >=age_m) ].groupby(['Quartile Number'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='GraphLiteracy_3', xlabel='WEALTH Quartile Number for >=median age',  yticks = np.arange(0, 4 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df4.loc[ (df4['age'] < age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] < age_m)  ].groupby(['Quartile Number'])['Bayesianreasoning_1'].mean(), yerr = df4.loc[ (df4['age'] < age_m) ].groupby(['Quartile Number'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Bayesianreasoning_1', xlabel='WEALTH Quartile Number for < median age',  yticks = np.arange(0, 5 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df4.loc[ (df4['age'] >= age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] >=age_m)  ].groupby(['Quartile Number'])['Bayesianreasoning_1'].mean(), yerr = df4.loc[ (df4['age'] >=age_m) ].groupby(['Quartile Number'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Bayesianreasoning_1', xlabel='WEALTH Quartile Number for >=median age',  yticks = np.arange(0, 5 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

	ResponseId	Please indicate your Gender.	Please mark your age (in years)	What is currently your highest Education?	Please state your current occupation.	How do you describe your willingness to take financial risk in general?	Given the number of years that you have held various investments and the amount of investing you might have done, what degree of investment experience in the stock market do you have?	If an expert tries to worry or scare me, i.e. a financial advisor about my financial situation, I choose another expert.	I only buy a financial product I understand.	I trust doctors.	When I want to buy a bigger item like a refrigerator or an expensive item of clothing, I wait a month to see whether I still want it and only buy it then.	I always keep in mind that everything I do on the web could be used to my disadvantage.	In my household, we/I spend:	In my household, we/I	_Distribution 1_\n\nHow risky do you perceive the investment to be?	_Distribution 2_\n\nHow risky do you perceive the investment to be?	_Distribution 3_\n\nHow risky do you perceive the investment to be?	_Distribution 4_\n\nHow risky do you perceive the investment to be?	_Distribution 5_\n\nHow risky do you perceive the investment to be?	Distribution 6\n\nHow risky do you perceive the investment to be?	Distribution 7\n\nHow risky do you perceive the investment to be?	Distribution 8\n\nHow risky do you perceive the investment to be?	Mumbai A = 9 out of 10000 OR B = 1 out of 1000	Bengaluru A = 0.7% OR B = 0.099%	Kolkata A = 0.61% OR B = 6 out of 10000	HIV test	Fingerprint	DNA test	Cancer screening test	Professional horoscope	A study estimates that eating 100g chocolate everyday increases the risk of obesity by 20%. Which of the following statements is true?	There is an official prediction that the national stock market will grow 2% annually over the next 5 years. This means that…	Imagine you are told that the price of the stock Soya Ruchi increases from INR 60 to INR 120 after the company merger. What does this mean?	It is predicted that Indigo Bank has 30% chance of default next year. Which of the following alternatives is the most appropriate interpretation of the statement?	The probability that the economy will go into a recession this year is 30%. If the economy goes into recession, the probability that the stock market will decrease is 80%. If the economy does not go into a recession, the probability that the stock market will decrease is 23%. What is the probability that the economy goes into recession given that the stock market decreased?	A new policy intervention increases the number of people who are employed by 20%. This statistic implies that the intervention increases the number of people who are employed from:	Imagine you are told that a new medication increases the number of people who recover from a disease from 2 out of 1,000 to 4 out of 1,000. This implies:	Imagine that we flip a fair coin 1,000 times. What is your best guess about how many times the coin will come up heads in 1,000 flips? \n\n\_\_\_\_\_\_ times out of 1,000.	In the Bingo Lottery, the chance of winning a $10 prize is 1%. What is your best guess about how many people will win a $10 prize if 1,000 people each buy a single ticket for Bingo Lottery?\n\n\_\_\_\_\_\_ person(s) out of 1,000.	In a sweepstakes, the chance of winning a car is 1 in 1,000. What percentage of tickets for the sweepstakes wins a car?\n\n\_\_\_\_\_ % of tickets	About 10 out of 1,000 children develop Down syndrome. In a Down syndrome test, 9 out of these 10 children with Down syndrome tested positive. Out of the 990 children without Down syndrome 50 nevertheless tested positive. Among those women with a positive test result concerning their child how many actually have a child with Down syndrome? [Select one response only]	Approximately what percentage (%) of people who die from cancer die from colon cancer, breast cancer, and prostate cancer taken together?	The following figure shows the number of men and women among a group of smartphone users. The total number of circles is 100. \n\nHow many more men than women are there among the 100 people using a smartphone?	In a magazine you see two advertisements, one on page 5 and another on page 12. Each is for a different drug for treating heart disease, and each includes a graph showing the effectiveness of the drug compared to a placebo (sugar pill).\n\nCompared to the placebo, which treatment leads to a larger decrease in the percentage of patients who die?	Please indicate your approximate annual personal income from all sources for last year	Please provide a rough guess (in Indian Rupees) of the worth of your household's assets. Please do not forget to correct it for your debts, such as a mortgage or any loans you might have.	uid	Response Type	Start Date (UTC)	Stage Date (UTC)	Submit Date (UTC)	Network ID	LTA
0	wjkzxyv1zx3q7oxgmtwwjwkq24ey2n48	Male	25 - 35	Post Graduate	Investment Professional, i.e. stock broker/tra...	5	8	Somewhat	Completely	Completely	Completely	Completely	more than half of the household income and sav...	write down budget and spending, and have an em...	6	6	7	7	4	3	7	7	A	B	A	Yes	Yes	Yes	No	Yes	Irrespective of the quality of the study, futu...	the growth rate over five years will be exactl...	the stock price increased by 50%	30% of central bankers think that Indigo Bank ...	60%	70 in 100 people prior to the intervention to ...	The medication increases recovery by 100%	50	100	1	59 out of 100	25	20	They are equal	5,00,000 - 15,00,000	100000	MX0O31725956661qRo	completed	2024-09-10 08:26:07	NaN	2024-09-10 08:34:54	89640d1b73	6.099537
1	fbv4e1fblmp0yreyfbv4eifpzx4ax7zs	Male	36 - 45	Ph.D. or higher	Investment Professional, i.e. stock broker/tra...	6	8	Just about	Completely	Completely	Just about	Completely	less than half of the household income and sav...	have an emergency fund, and spend as it feels ...	5	6	7	6	5	7	6	5	B	A	A	No	Yes	Yes	No	No	The lower the quality of the study, the more l...	the growth rate over five years will be exactl...	the stock price increased by 100%	30% of central bankers think that Indigo Bank ...	30%	70 in 100 people prior to the intervention to ...	None of the above is implied.	50	10	1	59 out of 1000	40	20	Crosicol	15,00,001 - 25,00,000	7200000	MXGDh1725956571Vdc	completed	2024-09-10 08:26:04	NaN	2024-09-10 08:36:25	e27c0ccdab	7.187500
2	6458w9qj8sqw7ldtt56457hzeduqz2qb	Female	36 - 45	Post Graduate	Employee/Consultant in Other than the Finance ...	6	10	Completely	Just about	Completely	Just about	Completely	all or more than the household income, even th...	have an emergency fund, and spend as it feels ...	7	3	6	7	6	7	6	7	B	A	B	Yes	Yes	Yes	Yes	Yes	The lower the quality of the study, the more l...	the growth rate over five years will be betwee...	the stock price increased by 100%	30% of central bankers think that Indigo Bank ...	80%	100 in 10,000 people prior to the intervention...	The medication increases recovery by 100%	10	100	10	59 out of 100	20	10	Crosicol	35,00,001 - 45,00,000	10	MXzTR1725956643Vmr	completed	2024-09-10 08:25:58	NaN	2024-09-10 08:33:57	942e517f31	5.543981
3	9woo3kquplxhob9whhbfvhleezgy3m8l	Male	25 - 35	Post Graduate	Employee/Consultant in Other than the Finance ...	5	3	Somewhat	Somewhat	Somewhat	Not at all	Somewhat	all or more than our household income, because...	write down budget and spending, and have an em...	4	2	4	1	1	7	6	3	A	B	A	Yes	Yes	Yes	No	No	Irrespective of the quality of the study, futu...	the growth rate over five years will be betwee...	the stock price increased by 50%	Banks similar to Indigo will default 30% of th...	30%	5 in 100 people prior to the intervention to 6...	The medication increases recovery by 2%	500	10	10	59 out of 1000	50	10	Hertinol	5,00,000 - 15,00,000	50000	MXhzL1725956594UqF	completed	2024-09-10 08:25:48	NaN	2024-09-10 08:35:16	dcc2ff7969	6.574074
4	bago2g5wtk2g5u2h0xbago2g00a6b3jx	Female	18 - 25	Post Graduate	Student	3	2	Somewhat	Just about	Completely	Completely	Just about	less than half of the household income and sav...	spend as it feel right, and do not have an eme...	3	4	6	3	1	7	7	5	B	A	B	Yes	Yes	Yes	No	No	The higher the quality of the study, the more ...	the growth rate will be 0.4% on average each year	the stock price increased by 50%	The bank will default on 30% of repayments in ...	80%	it is not possible to determine which of the a...	The medication increases recovery by 100%	500	10	10	9 out of 59	25	60	Hertinol	5,00,000 - 15,00,000	4000000	MXE5b1725956481kmw	completed	2024-09-10 08:25:12	NaN	2024-09-10 08:36:22	ed3799240f	7.754630
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
638	tod55nr27ozkyci5tod5rup87g8lo9ss	Male	18 - 25	Diploma or vocation training	Entrepreneur or Own Business	4	2	Somewhat	Moderately	Moderately	Somewhat	Moderately	all or more than our household income, because...	write down budget and spending, and have an em...	1	4	4	2	5	5	0	4	B	B	A	Yes	Yes	Yes	No	No	Irrespective of the quality of the study, futu...	the growth rate will be 0.4% on average each year	an answer is not possible based on the informa...	The bank will default on 30% of repayments in ...	50%	70 in 100 people prior to the intervention to ...	None of the above is implied.	500	10	1	59 out of 100	50	10	Hertinol	25,00,001 - 35,00,000	5000000	MXLz61724688143Vic	completed	2024-08-26 16:04:07	NaN	2024-08-26 16:12:43	e7e6df96c7	5.972222
639	phtgoyb2qh5lc4mhzq1fcphtgo8o361s	Male	25 - 35	Diploma or vocation training	Investment Professional, i.e. stock broker/tra...	7	10	Just about	Just about	Just about	Just about	Just about	less than half of the household income and sav...	write down budget and spending, but have no em...	7	7	7	7	7	6	2	3	B	B	B	Yes	Yes	Yes	Yes	Yes	Irrespective of the quality of the study, futu...	the growth rate will be 0.4% on average each year	the stock price increased by 60%	The bank will default on 30% of repayments in ...	50%	it is not possible to determine which of the a...	The medication increases recovery by 2%	1000	1000	10	59 out of 1000	50	100	They are equal	15,00,001 - 25,00,000	1000000	MXtZg1724688171JNW	completed	2024-08-26 16:04:00	NaN	2024-08-26 16:12:06	cfe4da8641	5.625000
640	c2ba33tn344exordvkc2baixvpwi0b7s	Female	25 - 35	Diploma or vocation training	Investment Professional, i.e. stock broker/tra...	1	2	Just about	Not at all	Just about	Just about	Not at all	all or more than the household income, even th...	write down budget and spending, but have no em...	0	0	1	1	0	0	2	0	A	A	B	No	Yes	Yes	No	Yes	The lower the quality of the study, the more l...	it is not possible to predict the growth rate ...	the stock price increased by 50%	The bank will default on 30% of repayments in ...	60%	it is not possible to determine which of the a...	The medication increases recovery by 100%	1000	500	10	59 out of 1000	10	60	They are equal	5,00,000 - 15,00,000	100000	MXlQt1724679500yOJ	completed	2024-08-26 13:41:51	NaN	2024-08-26 13:59:39	9eaccc3462	12.361111
641	13nxdmew77mtq97scc6r13nxduhpev0l	Male	25 - 35	Post Graduate	Entrepreneur or Own Business	5	9	Moderately	Moderately	Moderately	Moderately	Somewhat	less than half of the household income and sav...	have an emergency fund, and spend as it feels ...	3	3	5	1	0	3	7	2	A	B	A	Yes	Yes	Yes	Yes	No	The higher the quality of the study, the more ...	the growth rate will be 0.4% on average each year	the stock price increased by 100%	30% of the banks customers will default next year	30%	100 in 10,000 people prior to the intervention...	The medication increases recovery by 50%	500	100	1	59 out of 1000	25	60	Crosicol	15,00,001 - 25,00,000	13000000	MXcTq1724679467kFu	completed	2024-08-26 13:39:40	NaN	2024-08-26 13:49:13	5a7cce9582	6.631944
642	s5m2omt6lyzdyeb1k9cs5m2omtmv05c3	Female	18 - 25	Post Graduate	Employee/Consultant in Other than the Finance ...	6	1	Somewhat	Moderately	Moderately	Moderately	Moderately	more than half of the household income and sav...	write down budget and spending, and have an em...	5	0	6	1	6	6	0	6	A	A	A	Yes	Yes	Yes	Yes	No	The lower the quality of the study, the more l...	it is not possible to predict the growth rate ...	the stock price increased by 50%	The bank will default on 30% of repayments in ...	30%	70 in 100 people prior to the intervention to ...	The medication increases recovery by 50%	500	10	1	59 out of 1000	25	60	Crosicol	15,00,001 - 25,00,000	100000	MX4Er1724679488ZwW	completed	2024-08-26 13:39:25	NaN	2024-08-26 13:54:38	3e73e75dfb	10.567130

		ResponseId	Please indicate your Gender.	Please mark your age (in years)	What is currently your highest Education?	Please state your current occupation.	How do you describe your willingness to take financial risk in general?	Given the number of years that you have held various investments and the amount of investing you might have done, what degree of investment experience in the stock market do you have?	If an expert tries to worry or scare me, i.e. a financial advisor about my financial situation, I choose another expert.	I only buy a financial product I understand.	I trust doctors.	When I want to buy a bigger item like a refrigerator or an expensive item of clothing, I wait a month to see whether I still want it and only buy it then.	I always keep in mind that everything I do on the web could be used to my disadvantage.	In my household, we/I spend:	In my household, we/I	_Distribution 1_\n\nHow risky do you perceive the investment to be?	_Distribution 2_\n\nHow risky do you perceive the investment to be?	_Distribution 3_\n\nHow risky do you perceive the investment to be?	_Distribution 4_\n\nHow risky do you perceive the investment to be?	_Distribution 5_\n\nHow risky do you perceive the investment to be?	Distribution 6\n\nHow risky do you perceive the investment to be?	Distribution 7\n\nHow risky do you perceive the investment to be?	Distribution 8\n\nHow risky do you perceive the investment to be?	Mumbai A = 9 out of 10000 OR B = 1 out of 1000	Bengaluru A = 0.7% OR B = 0.099%	Kolkata A = 0.61% OR B = 6 out of 10000	HIV test	Fingerprint	DNA test	Cancer screening test	Professional horoscope	A study estimates that eating 100g chocolate everyday increases the risk of obesity by 20%. Which of the following statements is true?	There is an official prediction that the national stock market will grow 2% annually over the next 5 years. This means that…	Imagine you are told that the price of the stock Soya Ruchi increases from INR 60 to INR 120 after the company merger. What does this mean?	It is predicted that Indigo Bank has 30% chance of default next year. Which of the following alternatives is the most appropriate interpretation of the statement?	The probability that the economy will go into a recession this year is 30%. If the economy goes into recession, the probability that the stock market will decrease is 80%. If the economy does not go into a recession, the probability that the stock market will decrease is 23%. What is the probability that the economy goes into recession given that the stock market decreased?	A new policy intervention increases the number of people who are employed by 20%. This statistic implies that the intervention increases the number of people who are employed from:	Imagine you are told that a new medication increases the number of people who recover from a disease from 2 out of 1,000 to 4 out of 1,000. This implies:	Imagine that we flip a fair coin 1,000 times. What is your best guess about how many times the coin will come up heads in 1,000 flips? \n\n\_\_\_\_\_\_ times out of 1,000.	In the Bingo Lottery, the chance of winning a $10 prize is 1%. What is your best guess about how many people will win a $10 prize if 1,000 people each buy a single ticket for Bingo Lottery?\n\n\_\_\_\_\_\_ person(s) out of 1,000.	In a sweepstakes, the chance of winning a car is 1 in 1,000. What percentage of tickets for the sweepstakes wins a car?\n\n\_\_\_\_\_ % of tickets	About 10 out of 1,000 children develop Down syndrome. In a Down syndrome test, 9 out of these 10 children with Down syndrome tested positive. Out of the 990 children without Down syndrome 50 nevertheless tested positive. Among those women with a positive test result concerning their child how many actually have a child with Down syndrome? [Select one response only]	Approximately what percentage (%) of people who die from cancer die from colon cancer, breast cancer, and prostate cancer taken together?	The following figure shows the number of men and women among a group of smartphone users. The total number of circles is 100. \n\nHow many more men than women are there among the 100 people using a smartphone?	In a magazine you see two advertisements, one on page 5 and another on page 12. Each is for a different drug for treating heart disease, and each includes a graph showing the effectiveness of the drug compared to a placebo (sugar pill).\n\nCompared to the placebo, which treatment leads to a larger decrease in the percentage of patients who die?	Please indicate your approximate annual personal income from all sources for last year	Please provide a rough guess (in Indian Rupees) of the worth of your household's assets. Please do not forget to correct it for your debts, such as a mortgage or any loans you might have.	uid	Response Type	Start Date (UTC)	Stage Date (UTC)	Submit Date (UTC)	Network ID	LTA	age_rec	age	isced	income	wealth	q8_2_1	q8_2_2	q8_2_3	q8_2_4	q8_2_5	q8_3	q8_4	q8_5berlin_1	q8_5london_1	q8_5paris_1	q8_6	q8_7	q9_1_1	q9_2_1	q9_3	q10_1_1	q10_2_1	q10_3_1	q10_4	certainty1	certainty2	certainty3	certainty4	certainty5	uncertainty1	uncertainty2	numeracy1	numeracy2	numeracy3	numeracy4	numeracy5	graph1	graph2	graph3	riskcomprehension1	riskcomprehension2	riskcomprehension3	riskcomprehension4	riskcomprehension5	bayesian1
ResponseId
00dn8csjzlekx6gzhrcv00dn8csj32tz	547	00dn8csjzlekx6gzhrcv00dn8csj32tz	Male	25 - 35	Under Graduate	Investment Professional, i.e. stock broker/tra...	7	10	Completely	Just about	Completely	Completely	Just about	all or more than the household income, even th...	have an emergency fund, and spend as it feels ...	7	7	7	6	6	6	6	7	A	B	A	Yes	Yes	Yes	Yes	Yes	The lower the quality of the study, the more l...	the growth rate will be 0.4% on average each year	the stock price increased by 50%	30% of the banks customers will default next year	30%	it is not possible to determine which of the a...	The medication increases recovery by 50%	30	20	30	9 out of 10	40	60	They are equal	5,00,000 - 15,00,000	50000	MX9oX1724844773oYI	completed	2024-08-28 11:35:35	NaN	2024-08-28 12:02:05	f30fdd629e	18.402778	2	30.5	2	2	50000	1	1	1	1	1	1	1	1	2	1	2	4	40	60	3	30	20	30	2	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	1	0	1	0
00ujdxbfoya0donu8r00ujcjdkojc99x	428	00ujdxbfoya0donu8r00ujcjdkojc99x	Female	25 - 35	Under Graduate	Employee/Consultant in Other than the Finance ...	7	8	Completely	Completely	Completely	Moderately	Moderately	less than half of the household income and sav...	write down budget and spending, and have an em...	2	1	1	1	0	2	0	0	B	B	A	Yes	Yes	Yes	Yes	No	The higher the quality of the study, the more ...	the growth rate over five years will be betwee...	the stock price increased by 50%	The bank will default on 30% of repayments in ...	50%	100 in 10,000 people prior to the intervention...	The medication increases recovery by 50%	500	100	1	59 out of 1000	50	10	They are equal	< 5,00,000	1500000	MXic21725178744djB	completed	2024-09-01 08:23:04	NaN	2024-09-01 09:24:44	1c1d51349c	42.824074	2	30.5	2	1	1500000	1	1	1	1	2	2	3	2	2	1	2	2	50	10	3	500	100	1	3	0	0	0	0	1	0	0	1	0	0	0	0	0	0	1	1	0	1	0	0	0
01ro2iftnum5v4cn01r03c75aa24emhq	528	01ro2iftnum5v4cn01r03c75aa24emhq	Female	18 - 25	Under Graduate	Student	5	8	Just about	Completely	Completely	Completely	Just about	more than half of the household income and sav...	write down budget and spending, and have an em...	6	6	2	6	6	6	2	4	A	B	B	Yes	Yes	No	Yes	Yes	The lower the quality of the study, the more l...	the growth rate over five years will be exactl...	an answer is not possible based on the informa...	Banks similar to Indigo will default 30% of th...	50%	70 in 100 people prior to the intervention to ...	The medication increases recovery by 100%	46	100	99	59 out of 1000	79	40	Hertinol	5,00,000 - 15,00,000	500000	MX7wI1724844822zNm	completed	2024-08-28 11:36:50	NaN	2024-08-28 11:51:07	81a8a6836a	9.918981	2	21.5	2	2	500000	1	1	2	1	1	1	2	1	2	2	1	3	79	40	2	46	100	99	3	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
022xoawhrghfhv4a1g022xmz7hb0m41f	442	022xoawhrghfhv4a1g022xmz7hb0m41f	Female	46 - 55	Under Graduate	Entrepreneur or Own Business	5	4	Somewhat	Somewhat	Moderately	Just about	Somewhat	all or more than the household income, even th...	spend as it feel right, and do not have an eme...	4	4	4	4	4	4	4	4	B	A	B	Yes	Yes	Yes	No	No	The lower the quality of the study, the more l...	the growth rate will be 0.4% on average each year	the stock price increased by 60%	30% of the banks customers will default next year	80%	5 in 100 people prior to the intervention to 6...	The medication increases recovery by 2%	500	1	1	59 out of 1000	17	65	They are equal	< 5,00,000	15000000	MXFDH1725178659xRn	completed	2024-09-01 08:21:14	NaN	2024-09-01 08:33:49	fbd8962401	8.738426	3	50.5	2	1	15000000	1	1	1	2	2	1	1	2	1	2	3	1	17	65	3	500	1	1	3	0	0	0	1	1	1	0	1	0	0	0	0	0	0	1	1	1	0	0	0	0
02pxtdbyibecqqfvwlw02pxwfbane9zd	558	02pxtdbyibecqqfvwlw02pxwfbane9zd	Female	46 - 55	Post Graduate	Employee/Consultant in Other than the Finance ...	6	10	Not at all	Completely	Completely	Completely	Moderately	all or more than the household income, even th...	have an emergency fund, and spend as it feels ...	5	5	6	5	7	5	0	5	B	A	A	Yes	Yes	Yes	No	Yes	The higher the quality of the study, the more ...	it is not possible to predict the growth rate ...	an answer is not possible based on the informa...	30% of the banks customers will default next year	30%	5 in 100 people prior to the intervention to 6...	None of the above is implied.	1000	1	100	9 out of 10	20	20	Can’t say	5,00,000 - 15,00,000	3000000	MXIpM1724844742xet	completed	2024-08-28 11:35:05	NaN	2024-08-28 11:48:47	e8b668d11b	9.513889	3	50.5	1	2	3000000	1	1	1	2	1	2	4	2	1	1	4	1	20	20	4	1000	1	100	2	0	0	0	1	0	0	1	0	0	0	0	0	0	1	0	1	1	1	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
zeoufbqlngfunzzeoymhtj2sp5cw6lur	396	zeoufbqlngfunzzeoymhtj2sp5cw6lur	Male	25 - 35	Post Graduate	Investment Professional, i.e. stock broker/tra...	3	8	Not at all	Somewhat	Somewhat	Moderately	Somewhat	less than half of the household income and sav...	spend as it feel right, and do not have an eme...	3	4	4	3	6	6	3	4	B	A	A	No	Yes	No	No	No	The higher the quality of the study, the more ...	the growth rate over five years will be exactl...	the stock price increased by 100%	Banks similar to Indigo will default 30% of th...	50%	70 in 100 people prior to the intervention to ...	The medication increases recovery by 100%	500	100	1	59 out of 1000	50	40	Crosicol	< 5,00,000	50000	MXFoD1725258821KAh	completed	2024-09-02 06:36:01	NaN	2024-09-02 06:46:38	c31fa70c8d	7.372685	2	30.5	1	1	50000	2	1	2	2	2	2	2	2	1	1	1	3	50	40	1	500	100	1	3	1	0	1	1	1	0	0	1	0	0	0	0	0	0	0	1	1	1	1	0	0
zfemo30rg0ekva18x1kjwzfemo2wpg2p	185	zfemo30rg0ekva18x1kjwzfemo2wpg2p	Male	36 - 45	Ph.D. or higher	Employee/Consultant in Other than the Finance ...	6	8	Not at all	Moderately	Just about	Not at all	Not at all	less than half of the household income and sav...	have an emergency fund, and spend as it feels ...	3	1	4	2	0	5	0	2	B	A	A	No	Yes	Yes	No	No	Irrespective of the quality of the study, futu...	the growth rate over five years will be betwee...	the stock price increased by 100%	30% of central bankers think that Indigo Bank ...	30%	100 in 10,000 people prior to the intervention...	The medication increases recovery by 100%	500	10	1	59 out of 1000	25	20	Can’t say	15,00,001 - 25,00,000	8500000	MXUxt1725260651heG	completed	2024-09-02 07:08:18	NaN	2024-09-02 07:23:09	fbb956cffc	10.312500	3	40.5	1	3	8500000	2	1	1	2	2	4	3	2	1	1	1	2	25	20	4	500	10	1	3	1	0	0	1	1	0	0	1	1	0	0	0	1	1	0	1	1	1	1	0	0
znqvw4t38br3072znqvwvjsgb6wvj7nt	305	znqvw4t38br3072znqvwvjsgb6wvj7nt	Female	36 - 45	Post Graduate	Entrepreneur or Own Business	5	5	Somewhat	Completely	Just about	Moderately	Somewhat	less than half of the household income and sav...	spend as it feel right, and do not have an eme...	0	0	0	1	0	0	0	0	B	B	B	Yes	Yes	Yes	No	No	Irrespective of the quality of the study, futu...	it is not possible to predict the growth rate ...	the stock price increased by 50%	30% of the banks customers will default next year	80%	it is not possible to determine which of the a...	The medication increases recovery by 2%	500	10	1	9 out of 59	25	20	Hertinol	5,00,000 - 15,00,000	2000000	MXd921725259652XDJ	completed	2024-09-02 06:49:56	NaN	2024-09-02 07:17:20	1964f49e1c	19.027778	3	40.5	1	2	2000000	1	1	1	2	2	3	4	2	2	2	3	4	25	20	2	500	10	1	1	0	0	0	1	1	0	1	1	1	0	0	0	1	1	0	1	0	0	0	1	1
zstpq23h1x3ilab8s8vsrbkw8t4zstpq	306	zstpq23h1x3ilab8s8vsrbkw8t4zstpq	Male	18 - 25	Under Graduate	Entrepreneur or Own Business	6	9	Completely	Completely	Moderately	Completely	Moderately	all or more than our household income, because...	have an emergency fund, and spend as it feels ...	6	3	7	1	2	7	7	3	A	A	B	No	Yes	Yes	No	No	The higher the quality of the study, the more ...	the growth rate over five years will be betwee...	the stock price increased by 60%	Banks similar to Indigo will default 30% of th...	80%	5 in 100 people prior to the intervention to 6...	None of the above is implied.	750	10	1	59 out of 1000	50	10	They are equal	5,00,000 - 15,00,000	60000000	MXfmg1725259705Fkj	completed	2024-09-02 06:49:52	NaN	2024-09-02 07:01:04	03aa77ec48	7.777778	2	21.5	2	2	60000000	2	1	1	2	2	2	3	1	1	2	4	1	50	10	3	750	10	1	3	1	0	0	1	1	0	0	0	1	0	0	0	0	0	1	0	1	0	0	0	0
ztfdlcbiyo1xco3ztfdl4ozqpte2q3m9	95	ztfdlcbiyo1xco3ztfdl4ozqpte2q3m9	Male	25 - 35	Post Graduate	Entrepreneur or Own Business	6	7	Somewhat	Just about	Completely	Moderately	Somewhat	more than half of the household income and sav...	spend as it feel right, and do not have an eme...	6	0	2	5	3	7	0	4	B	A	A	No	Yes	Yes	Yes	Yes	The higher the quality of the study, the more ...	it is not possible to predict the growth rate ...	the stock price increased by 100%	30% of central bankers think that Indigo Bank ...	80%	5 in 100 people prior to the intervention to 6...	The medication increases recovery by 2%	500	10	1	59 out of 1000	25	60	Can’t say	15,00,001 - 25,00,000	5000000	MXjp51725955476hMt	completed	2024-09-10 08:06:34	NaN	2024-09-10 08:16:35	f68294192e	6.956019	2	30.5	1	3	5000000	2	1	1	1	1	2	4	2	1	1	3	1	25	60	4	500	10	1	3	1	0	0	0	0	0	1	1	1	0	0	0	1	0	0	1	1	1	0	0	0

		ResponseId	age	age_rec	isced	income	wealth	Certainty_5	Uncertainty_2	RiskComprehension_5	GraphLiteracy_3	Numeracy_2	Bayesianreasoning_1	Certainty_%	Uncertainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_18	TotalScore_%
ResponseId
00dn8csjzlekx6gzhrcv00dn8csj32tz	547	00dn8csjzlekx6gzhrcv00dn8csj32tz	30.5	2	2	2	50000	0	1	2	1	0	0	0.0	50.0	40.0	33.333333	0.0	0.0	4	22.222222
00ujdxbfoya0donu8r00ujcjdkojc99x	428	00ujdxbfoya0donu8r00ujcjdkojc99x	30.5	2	2	1	1500000	1	0	2	1	1	0	20.0	0.0	40.0	33.333333	50.0	0.0	5	27.777778
01ro2iftnum5v4cn01r03c75aa24emhq	528	01ro2iftnum5v4cn01r03c75aa24emhq	21.5	2	2	2	500000	1	1	1	0	0	0	20.0	50.0	20.0	0.000000	0.0	0.0	3	16.666667
022xoawhrghfhv4a1g022xmz7hb0m41f	442	022xoawhrghfhv4a1g022xmz7hb0m41f	50.5	3	2	1	15000000	2	1	2	1	1	0	40.0	50.0	40.0	33.333333	50.0	0.0	7	38.888889
02pxtdbyibecqqfvwlw02pxwfbane9zd	558	02pxtdbyibecqqfvwlw02pxwfbane9zd	50.5	3	1	2	3000000	1	1	3	1	0	0	20.0	50.0	60.0	33.333333	0.0	0.0	6	33.333333
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
zeoufbqlngfunzzeoymhtj2sp5cw6lur	396	zeoufbqlngfunzzeoymhtj2sp5cw6lur	30.5	2	1	1	50000	4	0	4	0	1	0	80.0	0.0	80.0	0.000000	50.0	0.0	9	50.000000
zfemo30rg0ekva18x1kjwzfemo2wpg2p	185	zfemo30rg0ekva18x1kjwzfemo2wpg2p	40.5	3	1	3	8500000	3	0	4	2	2	0	60.0	0.0	80.0	66.666667	100.0	0.0	11	61.111111
znqvw4t38br3072znqvwvjsgb6wvj7nt	305	znqvw4t38br3072znqvwvjsgb6wvj7nt	40.5	3	1	2	2000000	2	1	2	2	2	1	40.0	50.0	40.0	66.666667	100.0	100.0	10	55.555556
zstpq23h1x3ilab8s8vsrbkw8t4zstpq	306	zstpq23h1x3ilab8s8vsrbkw8t4zstpq	21.5	2	2	2	60000000	3	0	1	1	1	0	60.0	0.0	20.0	33.333333	50.0	0.0	6	33.333333
ztfdlcbiyo1xco3ztfdl4ozqpte2q3m9	95	ztfdlcbiyo1xco3ztfdl4ozqpte2q3m9	30.5	2	1	3	5000000	1	1	3	1	2	0	20.0	50.0	60.0	33.333333	100.0	0.0	8	44.444444

		ResponseId	Q0	Q1	Q2	Q3	Q4	Q5_1	Q6_1	Q8_1	Q8_2	Q8_3	Q8_4	Q8_5	Q8_6	Q8_7	Q8_8	Q9b_1	Q9b_4	Q9b_5	Q9b_6	Q9b_7	Q9b_8	Q9b_9	Q11a_1	Q11a_2	Q11a_3	Q11b	Q11c	Q11d	Q11h	Attention Check	Q11i	Q12a	Q12b	Q12c	Q13a	Q13b	Q13c	Q13d	Q14a	Q14b	Q14c	Q15a	Q14b.1	age	age_rec	isced	income	wealth	q8_2_1	q8_2_2	q8_2_3	q8_2_4	q8_2_5	q8_3	q8_4	q8_5berlin_1	q8_5london_1	q8_5paris_1	q8_6	q8_7	q9_1_1	q9_2_1	q9_3	q10_1_1	q10_2_1	q10_3_1	q10_4	certainty1	certainty2	certainty3	certainty4	certainty5	uncertainty1	uncertainty2	numeracy1	numeracy2	numeracy3	numeracy4	numeracy5	graph1	graph2	graph3	riskcomprehension1	riskcomprehension2	riskcomprehension3	riskcomprehension4	riskcomprehension5	bayesian1
ResponseId
R_1YkMM2lMB9aEuVL	150	R_1YkMM2lMB9aEuVL	Yes, I would like to participate in the study ...	Female	41	Undergraduate Program	Salaried/Employee/Consultant in a sector other...	3	3	3	4	3	3	3	3	3	4	85.0	0.0	0	0	5.0	10	0.0	1	1	1	DNA test	The higher the quality of the study, the more ...	The growth rate over five years will be betwee...	5 in 100 people prior to the intervention to 6...	Vase	The medication increases recovery by 50%	Less than $102	Exactly the same as today with the money in th...	True	1000	10	1.000	59 out of 1000	9.0	20	Crosicol	< INR 500,000	25000.0	41	3	2	1	25000.0	2	2	1	2	2	2	3	1	1	1	2	1	9.0	20	1	1000	10	1.000	3	1	1	0	1	1	0	0	0	1	0	0	0	0	1	0	0	1	1	0	0	0
R_401xXJ9E3ZgU9gu	96	R_401xXJ9E3ZgU9gu	Yes, I would like to participate in the study ...	Male	33	Undergraduate Program	Stock analyst	7 (willing to take risk)	8	5	4	5	5	5	6 (strongly agree)	6 (strongly agree)	6 (strongly agree)	0.0	0.0	10	0	30.0	60	0.0	2	1	1	Fingerprint,DNA test	The higher the quality of the study, the more ...	The growth rate over five years will be betwee...	100 in 10,000 people prior to the intervention...	Vase	The medication increases recovery by 100%	More than $102	Less than today with the money in this account	False	500	1	0.001	59 out of 1000	25.0	20	They are equal	INR 500,001 – INR 15,00,000	16000000.0	33	2	2	2	16000000.0	2	1	1	2	2	2	3	2	2	2	1	2	25.0	20	3	500	1	0.001	3	1	0	0	1	1	0	0	1	0	0	0	0	1	1	1	1	0	0	1	0	0
R_40N8bUn3C0jwGzf	200	R_40N8bUn3C0jwGzf	Yes, I would like to participate in the study ...	Female	18	Undergraduate Program	Student	0 (unwilling to take risk)	0 (no investment experience)	3	3	4	3	3	3	2	3	40.0	20.0	10	10	10.0	5	5.0	2	1	1	HIV test,Fingerprint,DNA test,Cancer screening...	The higher the quality of the study, the more ...	The growth rate over five years will be betwee...	It is not possible to determine which of the a...	Vase	The medication increases recovery by 2%	More than $102	More than today with the money in this account	True	1000	500	100.000	9 out of 59	11.0	60	They are equal	< INR 500,000	80000.0	18	2	2	1	80000.0	1	1	1	1	2	2	3	2	2	2	3	4	11.0	60	3	1000	500	100.000	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	1
R_40VyTmJ6i96wUP4	235	R_40VyTmJ6i96wUP4	Yes, I would like to participate in the study ...	Female	34	Post-Graduate Program	Not employed	2	0 (no investment experience)	3	2	2	3	3	3	2	3	40.0	25.0	10	10	15.0	0	0.0	1	2	1	DNA test	The higher the quality of the study, the more ...	It is not possible to predict the growth rate ...	It is not possible to determine which of the a...	Vase	The medication increases recovery by 2%	Refuse to answer	Less than today with the money in this account	Do not know	698	352	1.000	59 out of 1000	30.0	20	Crosicol	< INR 500,000	500000.0	34	2	1	1	500000.0	2	2	1	2	2	2	4	1	1	1	3	4	30.0	20	1	698	352	1.000	3	1	1	0	1	1	0	1	0	0	0	0	0	0	1	0	0	1	1	0	1	0
R_40ZEg7vX3Y7mMQl	184	R_40ZEg7vX3Y7mMQl	Yes, I would like to participate in the study ...	Female	31	Undergraduate Program	Not employed	4	6	2	2	2	1 (strongly disagree)	2	3	4	1 (strongly disagree)	30.0	30.0	10	0	20.0	10	0.0	1	1	1	HIV test,Fingerprint,DNA test,Cancer screening...	The higher the quality of the study, the more ...	The growth rate will be 0.4% on average each year	70 in 100 people prior to the intervention to ...	Vase	The medication increases recovery by 2%	More than $102	Less than today with the money in this account	True	500	10	10.000	59 out of 1000	50.0	20	They are equal	< INR 500,000	500000.0	31	2	2	1	500000.0	1	1	1	1	2	2	1	1	1	1	3	3	50.0	20	3	500	10	10.000	3	0	0	0	0	1	0	0	1	1	0	0	0	0	1	1	0	1	1	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
R_4yzFrFYMa7w8nQZ	238	R_4yzFrFYMa7w8nQZ	Yes, I would like to participate in the study ...	Female	18	Secondary School (11th to 12th Std.)	Student	0 (unwilling to take risk)	0 (no investment experience)	6 (strongly agree)	5	5	5	4	4	5	6 (strongly agree)	20.0	10.0	15	25	5.0	9	16.0	1	2	1	DNA test	The higher the quality of the study, the more ...	The growth rate over five years will be exactl...	70 in 100 people prior to the intervention to ...	Vase	The medication increases recovery by 100%	More than $102	Less than today with the money in this account	True	1000	100	100.000	59 out of 1000	25.0	20	Hertinol	< INR 500,000	90000.0	18	2	3	1	90000.0	2	2	1	2	2	2	2	1	1	1	1	3	25.0	20	2	1000	100	100.000	3	1	1	0	1	1	0	0	0	0	0	0	0	1	1	0	0	1	1	1	0	0
R_4zPiSk23ayGvFD4	364	R_4zPiSk23ayGvFD4	Yes, I would like to participate in the study ...	Male	70	Undergraduate Program	Salaried/Employee/Consultant in a sector other...	4	6	4	4	4	4	5	5	5	4	15.0	20.0	10	0	25.0	30	0.0	2	1	1	DNA test	The higher the quality of the study, the more ...	It is not possible to predict the growth rate ...	It is not possible to determine which of the a...	Vase	The medication increases recovery by 100%	More than $102	Less than today with the money in this account	False	500	10	1.000	59 out of 1000	25.0	20	Crosicol	INR 1500,001 – INR 30,00,000	7000000.0	70	4	2	3	7000000.0	2	2	1	2	2	2	4	2	2	2	1	4	25.0	20	1	500	10	1.000	3	1	1	0	1	1	0	1	1	1	0	0	0	1	1	0	1	0	0	1	1	0
R_4zdzjECYeO2A5Ox	255	R_4zdzjECYeO2A5Ox	Yes, I would like to participate in the study ...	Female	19	Secondary School (11th to 12th Std.)	Student	4	2	6 (strongly agree)	4	5	5	5	6 (strongly agree)	3	4	40.0	50.0	0	10	0.0	0	0.0	2	1	1	Fingerprint,DNA test	The lower the quality of the study, the more l...	The growth rate over five years will be exactl...	70 in 100 people prior to the intervention to ...	Vase	None of the above is implied	More than $102	Less than today with the money in this account	Do not know	500	10	0.100	9 out of 59	25.0	20	They are equal	< INR 500,000	800000.0	19	2	3	1	800000.0	2	1	1	2	2	1	2	2	2	2	4	3	25.0	20	3	500	10	0.100	1	1	0	0	1	1	1	0	1	1	1	0	0	1	1	1	1	0	0	0	0	1
R_8k6D0jzzHCC5X3Z	369	R_8k6D0jzzHCC5X3Z	Yes, I would like to participate in the study ...	Male	25	Undergraduate Program	Salaried/Employee/Consultant in a sector other...	7 (willing to take risk)	8	6 (strongly agree)	5	5	4	6 (strongly agree)	6 (strongly agree)	6 (strongly agree)	5	5.0	5.0	5	5	30.0	20	30.0	2	1	1	DNA test	The higher the quality of the study, the more ...	It is not possible to predict the growth rate ...	5 in 100 people prior to the intervention to 6...	Vase	The medication increases recovery by 100%	More than $102	Less than today with the money in this account	False	500	500	50.000	59 out of 1000	25.0	10	Hertinol	> INR 75,00,000	8000000.0	25	2	2	5	8000000.0	2	2	1	2	2	2	4	2	2	2	1	1	25.0	10	2	500	500	50.000	3	1	1	0	1	1	0	1	1	0	0	0	0	1	0	0	1	0	0	1	0	0
R_8taN3wUPbeZMxzM	2	R_8taN3wUPbeZMxzM	Yes, I would like to participate in the study ...	Male	20	Undergraduate Program	Student	3	0 (no investment experience)	6 (strongly agree)	6 (strongly agree)	4	1 (strongly disagree)	1 (strongly disagree)	2	1 (strongly disagree)	6 (strongly agree)	30.0	40.0	10	10	0.0	5	5.0	1	1	1	Fingerprint	The higher the quality of the study, the more ...	It is not possible to predict the growth rate ...	70 in 100 people prior to the intervention to ...	Vase	The medication increases recovery by 50%	More than $102	More than today with the money in this account	Do not know	1000	10	50.000	9 out of 59	50.0	20	They are equal	< INR 500,000	10000.0	20	2	2	1	10000.0	2	1	2	2	2	2	4	1	1	1	2	3	50.0	20	3	1000	10	50.000	1	1	0	1	1	1	0	1	0	1	0	0	0	0	1	1	0	1	1	0	0	1

	ResponseId	age	age_rec	isced	income	wealth	Certainty_5	Uncertainty_2	RiskComprehension_5	GraphLiteracy_3	Numeracy_2	Bayesianreasoning_1	Certainty_%	Uncertainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_18	TotalScore_%
0	R_1YkMM2lMB9aEuVL	41	3	2	1	25000.0	4	0	2	1	1	0	80.0	0.0	40.0	33.333333	50.0	0.0	8	44.444444
1	R_401xXJ9E3ZgU9gu	33	2	2	2	16000000.0	3	0	2	3	1	0	60.0	0.0	40.0	100.000000	50.0	0.0	9	50.000000
2	R_40N8bUn3C0jwGzf	18	2	2	1	80000.0	1	0	2	1	0	1	20.0	0.0	40.0	33.333333	0.0	100.0	5	27.777778
3	R_40VyTmJ6i96wUP4	34	2	1	1	500000.0	4	1	3	1	0	0	80.0	50.0	60.0	33.333333	0.0	0.0	9	50.000000
4	R_40ZEg7vX3Y7mMQl	31	2	2	1	500000.0	1	0	2	2	2	0	20.0	0.0	40.0	66.666667	100.0	0.0	7	38.888889
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
365	R_4yzFrFYMa7w8nQZ	18	2	3	1	90000.0	4	0	3	2	0	0	80.0	0.0	60.0	66.666667	0.0	0.0	9	50.000000
366	R_4zPiSk23ayGvFD4	70	4	2	3	7000000.0	4	1	3	2	2	0	80.0	50.0	60.0	66.666667	100.0	0.0	12	66.666667
367	R_4zdzjECYeO2A5Ox	19	2	3	1	800000.0	3	1	1	3	2	1	60.0	50.0	20.0	100.000000	100.0	100.0	11	61.111111
368	R_8k6D0jzzHCC5X3Z	25	2	2	5	8000000.0	4	1	2	1	1	0	80.0	50.0	40.0	33.333333	50.0	0.0	9	50.000000
369	R_8taN3wUPbeZMxzM	20	2	2	1	10000.0	4	1	2	2	1	1	80.0	50.0	40.0	66.666667	50.0	100.0	11	61.111111

DATA CLEANING AND WRANGLING¶

Typeform Data¶

Demographics¶

Questions¶

Scoring¶

BeSample Data¶

Scoring¶

GRAPHS¶

ISCED¶

INCOME¶

INCOME VS SCORES¶

INCOME vs SCORES w/ ISCED classification¶

INCOME vs SCORES w/ ISCED and AGE based classification¶

WEALTH¶

WEALTH vs SCORES w/ ISCED classification¶

WEALTH Quartile Number vs SCORES w/ ISCED and AGE based classification¶

	q8_2_1	q8_2_2	q8_2_3	q8_2_4	q8_2_5	q8_3	q8_4	q8_5berlin_1	q8_5london_1	q8_5paris_1	q8_6	q8_7	q9_1_1	q9_2_1	q9_3	q10_1_1	q10_2_1	q10_3_1	q10_4
0	1	2	2	2	1	4	3	2	2	2	1	1	25.0	20	3	500	10	0.10	2
1	1	1	1	2	2	4	4	2	2	2	1	2	25.0	20	4	500	10	0.10	3
2	2	1	2	2	2	2	4	1	1	1	2	3	50.0	20	3	1000	10	50.00	1
3	2	1	1	2	2	2	2	1	1	1	2	2	25.0	20	3	500	10	0.10	3
4	1	1	1	1	2	2	2	2	2	2	1	2	25.0	20	3	500	10	0.10	3
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
365	2	2	2	2	1	3	2	1	1	1	3	3	50.0	40	3	500	68	0.01	4
366	2	2	2	1	2	2	2	2	2	2	3	4	25.0	20	3	500	50	0.10	1
367	2	2	1	2	2	3	4	1	1	1	3	4	90.0	20	3	345	1	0.10	2
368	1	1	1	1	2	2	3	2	2	2	1	3	25.0	5	2	600	10	0.10	2
369	2	2	1	2	2	2	4	2	2	2	1	1	25.0	10	2	500	500	50.00	3

	ResponseId	age	age_rec	isced	income	wealth	Certainty_5	Uncertainty_2	RiskComprehension_5	GraphLiteracy_3	Numeracy_2	Bayesianreasoning_1	Certainty_%	Uncertainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_18	TotalScore_%
0	00dn8csjzlekx6gzhrcv00dn8csj32tz	30.5	2	2	2	50000.0	0	1	2	1	0	0	0.0	50.0	40.0	33.333333	0.0	0.0	4	22.222222
1	00ujdxbfoya0donu8r00ujcjdkojc99x	30.5	2	2	1	1500000.0	1	0	2	1	1	0	20.0	0.0	40.0	33.333333	50.0	0.0	5	27.777778
2	01ro2iftnum5v4cn01r03c75aa24emhq	21.5	2	2	2	500000.0	1	1	1	0	0	0	20.0	50.0	20.0	0.000000	0.0	0.0	3	16.666667
3	022xoawhrghfhv4a1g022xmz7hb0m41f	50.5	3	2	1	15000000.0	2	1	2	1	1	0	40.0	50.0	40.0	33.333333	50.0	0.0	7	38.888889
4	02pxtdbyibecqqfvwlw02pxwfbane9zd	50.5	3	1	2	3000000.0	1	1	3	1	0	0	20.0	50.0	60.0	33.333333	0.0	0.0	6	33.333333
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1008	R_4yzFrFYMa7w8nQZ	18.0	2	3	1	90000.0	4	0	3	2	0	0	80.0	0.0	60.0	66.666667	0.0	0.0	9	50.000000
1009	R_4zPiSk23ayGvFD4	70.0	4	2	3	7000000.0	4	1	3	2	2	0	80.0	50.0	60.0	66.666667	100.0	0.0	12	66.666667
1010	R_4zdzjECYeO2A5Ox	19.0	2	3	1	800000.0	3	1	1	3	2	1	60.0	50.0	20.0	100.000000	100.0	100.0	11	61.111111
1011	R_8k6D0jzzHCC5X3Z	25.0	2	2	5	8000000.0	4	1	2	1	1	0	80.0	50.0	40.0	33.333333	50.0	0.0	9	50.000000
1012	R_8taN3wUPbeZMxzM	20.0	2	2	1	10000.0	4	1	2	2	1	1	80.0	50.0	40.0	66.666667	50.0	100.0	11	61.111111

	ResponseId	age	age_rec	isced	income	wealth	Certainty_5	Uncertainty_2	RiskComprehension_5	GraphLiteracy_3	Numeracy_2	Bayesianreasoning_1	Certainty_%	Uncertainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_18	TotalScore_%
0	ssrs382tg88lrssrnbo164l29aginpp7	40.5	3	1	4	12000000.0	2	0	4	2	2	1	40.0	0.0	80.0	66.666667	100.0	100.0	11	61.111111
1	o4gmm9twzl8mnxkt4nc3o4gmpxu1o787	30.5	2	1	2	100000.0	0	0	1	2	2	0	0.0	0.0	20.0	66.666667	100.0	0.0	5	27.777778
2	o7cqrckiw88li0ww70jwhb7o7cqrcki9	60.5	4	1	2	2000000.0	2	2	4	3	2	0	40.0	100.0	80.0	100.000000	100.0	0.0	13	72.222222
3	obftinjncjw2fkari22ghobfti6alubg	30.5	2	1	3	5200000.0	2	0	4	3	1	0	40.0	0.0	80.0	100.000000	50.0	0.0	10	55.555556
4	odfxscnhx6pz1ptrodfxs46gbnyv7ntb	60.5	4	1	2	1250000.0	2	1	2	2	1	0	40.0	50.0	40.0	66.666667	50.0	0.0	8	44.444444
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1008	sw8rv2orxir5asm6jn8sw8rv24olibmz	21.5	2	3	1	30000.0	1	0	3	0	1	0	20.0	0.0	60.0	0.000000	50.0	0.0	5	27.777778
1009	t1hiz7zjzou3pfd59pqt1hizrglay7e5	40.5	3	3	1	200000.0	3	0	1	2	1	0	60.0	0.0	20.0	66.666667	50.0	0.0	7	38.888889
1010	tljgvfjwoh3aj1i4hdk8wtphtljgjahd	70.5	4	3	1	2500000.0	2	1	3	3	1	0	40.0	50.0	60.0	100.000000	50.0	0.0	10	55.555556
1011	R_4o6HCiZjPLlcuI1	19.0	2	3	3	200.0	4	0	3	0	0	0	80.0	0.0	60.0	0.000000	0.0	0.0	7	38.888889
1012	R_4aQfSYuVQoXAOIh	30.0	2	3	5	50000.0	4	1	2	1	1	0	80.0	50.0	40.0	33.333333	50.0	0.0	9	50.000000

	Certainty_%	Uncertainty_%	RiskComprehension_%	Numeracy_%	GraphLiteracy_%	Bayesianreasoning_%
isced
1	44.067797	21.939736	43.766478	52.542373	42.247332	19.209040
2	51.558753	22.182254	44.220624	53.956835	45.323741	25.659472
3	62.461538	33.076923	42.461538	46.923077	42.564103	23.076923

	Certainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_%
income
1	57.592593	43.271605	47.016461	52.623457	24.691358	46.004801
2	44.392765	44.392765	44.961240	55.943152	20.671835	41.903531
3	45.256410	46.282051	39.316239	49.679487	20.512821	40.954416
4	40.618557	42.268041	37.800687	48.453608	20.618557	38.144330
5	43.265306	39.183673	34.013605	46.938776	24.489796	37.301587

	age	age_rec	isced	income	wealth	Certainty_5	Uncertainty_2	RiskComprehension_5	GraphLiteracy_3	Numeracy_2	Bayesianreasoning_1	Certainty_%	Uncertainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_18	TotalScore_%
count	531.000000	531.000000	531.0	531.000000	5.310000e+02	531.000000	531.000000	531.000000	531.000000	531.000000	531.000000	531.000000	531.000000	531.000000	531.000000	531.000000	531.000000	531.000000	531.000000
mean	34.803202	2.425612	1.0	2.448211	1.043356e+07	2.203390	0.438795	2.188324	1.267420	1.050847	0.192090	44.067797	21.939736	43.766478	42.247332	52.542373	19.209040	7.340866	40.782591
std	9.256072	0.562695	0.0	1.120416	4.024320e+07	1.290045	0.599938	1.138658	0.965466	0.771701	0.394315	25.800891	29.996920	22.773165	32.182211	38.585064	39.431522	2.622581	14.569894
min	18.000000	2.000000	1.0	1.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	30.500000	2.000000	1.0	2.000000	4.000000e+05	1.000000	0.000000	1.000000	1.000000	0.000000	0.000000	20.000000	0.000000	20.000000	33.333333	0.000000	0.000000	5.500000	30.555556
50%	30.500000	2.000000	1.0	2.000000	1.500000e+06	2.000000	0.000000	2.000000	1.000000	1.000000	0.000000	40.000000	0.000000	40.000000	33.333333	50.000000	0.000000	7.000000	38.888889
75%	40.500000	3.000000	1.0	3.000000	6.000000e+06	3.000000	1.000000	3.000000	2.000000	2.000000	0.000000	60.000000	50.000000	60.000000	66.666667	100.000000	0.000000	9.000000	50.000000
max	70.500000	4.000000	1.0	5.000000	5.000000e+08	5.000000	2.000000	5.000000	3.000000	2.000000	1.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	15.000000	83.333333

	age	age_rec	isced	income	wealth	Certainty_5	Uncertainty_2	RiskComprehension_5	GraphLiteracy_3	Numeracy_2	Bayesianreasoning_1	Certainty_%	Uncertainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_18	TotalScore_%
count	417.000000	417.000000	417.0	417.000000	4.170000e+02	417.000000	417.000000	417.000000	417.000000	417.000000	417.000000	417.000000	417.000000	417.000000	417.000000	417.000000	417.000000	417.000000	417.000000
mean	30.781775	2.263789	2.0	1.896882	9.459245e+06	2.577938	0.443645	2.211031	1.359712	1.079137	0.256595	51.558753	22.182254	44.220624	45.323741	53.956835	25.659472	7.928058	44.044764
std	9.887607	0.507120	0.0	1.031436	4.913786e+07	1.335280	0.618088	1.050903	0.997645	0.818048	0.437278	26.705607	30.904402	21.018061	33.254842	40.902417	43.727842	2.670999	14.838883
min	18.000000	2.000000	2.0	1.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	5.555556
25%	22.000000	2.000000	2.0	1.000000	2.000000e+05	1.000000	0.000000	1.000000	1.000000	0.000000	0.000000	20.000000	0.000000	20.000000	33.333333	0.000000	0.000000	6.000000	33.333333
50%	30.500000	2.000000	2.0	2.000000	6.900000e+05	3.000000	0.000000	2.000000	1.000000	1.000000	0.000000	60.000000	0.000000	40.000000	33.333333	50.000000	0.000000	8.000000	44.444444
75%	34.000000	2.000000	2.0	2.000000	4.000000e+06	4.000000	1.000000	3.000000	2.000000	2.000000	1.000000	80.000000	50.000000	60.000000	66.666667	100.000000	100.000000	10.000000	55.555556
max	70.000000	4.000000	2.0	5.000000	6.000000e+08	5.000000	2.000000	5.000000	3.000000	2.000000	1.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	15.000000	83.333333

	age	age_rec	isced	income	wealth	Certainty_5	Uncertainty_2	RiskComprehension_5	GraphLiteracy_3	Numeracy_2	Bayesianreasoning_1	Certainty_%	Uncertainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_18	TotalScore_%
count	65.000000	65.000000	65.0	65.000000	6.500000e+01	65.000000	65.000000	65.000000	65.000000	65.000000	65.000000	65.000000	65.000000	65.000000	65.000000	65.000000	65.000000	65.000000	65.000000
mean	27.138462	2.230769	3.0	1.661538	3.763119e+06	3.123077	0.661538	2.123077	1.276923	0.938462	0.230769	62.461538	33.076923	42.461538	42.564103	46.923077	23.076923	8.353846	46.410256
std	12.124519	0.580119	0.0	1.135739	1.307520e+07	1.192525	0.667947	0.910146	0.976404	0.826834	0.424604	23.850496	33.397374	18.202916	32.546810	41.341681	42.460389	2.546019	14.144549
min	16.000000	0.000000	3.0	1.000000	0.000000e+00	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	20.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.000000	16.666667
25%	19.000000	2.000000	3.0	1.000000	5.000000e+04	2.000000	0.000000	2.000000	0.000000	0.000000	0.000000	40.000000	0.000000	40.000000	0.000000	0.000000	0.000000	7.000000	38.888889
50%	21.500000	2.000000	3.0	1.000000	2.000000e+05	4.000000	1.000000	2.000000	1.000000	1.000000	0.000000	80.000000	50.000000	40.000000	33.333333	50.000000	0.000000	9.000000	50.000000
75%	30.500000	2.000000	3.0	2.000000	2.000000e+06	4.000000	1.000000	3.000000	2.000000	2.000000	0.000000	80.000000	50.000000	60.000000	66.666667	100.000000	0.000000	10.000000	55.555556
max	70.500000	4.000000	3.0	5.000000	1.000000e+08	5.000000	2.000000	4.000000	3.000000	2.000000	1.000000	100.000000	100.000000	80.000000	100.000000	100.000000	100.000000	12.000000	66.666667

	age	age_rec	isced	income	wealth	Certainty_5	Uncertainty_2	RiskComprehension_5	GraphLiteracy_3	Numeracy_2	Bayesianreasoning_1	Certainty_%	Uncertainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_18	TotalScore_%
count	693.000000	693.0	693.000000	693.000000	6.930000e+02	693.000000	693.000000	693.000000	693.000000	693.000000	693.00000	693.000000	693.000000	693.000000	693.000000	693.000000	693.000000	693.000000	693.000000
mean	27.116162	2.0	1.603175	2.059163	7.513477e+06	2.473304	0.458874	2.155844	1.326118	1.057720	0.23088	49.466089	22.943723	43.116883	44.203944	52.886003	23.088023	7.702742	42.793009
std	4.547104	0.0	0.617511	1.083624	3.290550e+07	1.350849	0.629338	1.069256	0.978303	0.802535	0.42170	27.016973	31.466902	21.385112	32.610103	40.126750	42.170032	2.639946	14.666365
min	18.000000	2.0	1.000000	1.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	22.000000	2.0	1.000000	1.000000	2.000000e+05	1.000000	0.000000	1.000000	1.000000	0.000000	0.00000	20.000000	0.000000	20.000000	33.333333	0.000000	0.000000	6.000000	33.333333
50%	30.500000	2.0	2.000000	2.000000	8.000000e+05	2.000000	0.000000	2.000000	1.000000	1.000000	0.00000	40.000000	0.000000	40.000000	33.333333	50.000000	0.000000	8.000000	44.444444
75%	30.500000	2.0	2.000000	3.000000	3.500000e+06	4.000000	1.000000	3.000000	2.000000	2.000000	0.00000	80.000000	50.000000	60.000000	66.666667	100.000000	0.000000	10.000000	55.555556
max	35.000000	2.0	3.000000	5.000000	5.000000e+08	5.000000	2.000000	5.000000	3.000000	2.000000	1.00000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	15.000000	83.333333

	age	age_rec	isced	income	wealth	Certainty_5	Uncertainty_2	RiskComprehension_5	GraphLiteracy_3	Numeracy_2	Bayesianreasoning_1	Certainty_%	Uncertainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_18	TotalScore_%
count	285.000000	285.0	285.000000	285.000000	2.850000e+02	285.000000	285.000000	285.000000	285.000000	285.000000	285.000000	285.000000	285.000000	285.000000	285.000000	285.000000	285.000000	285.000000	285.000000
mean	42.577193	3.0	1.385965	2.445614	1.463604e+07	2.277193	0.445614	2.266667	1.235088	1.042105	0.196491	45.543860	22.280702	45.333333	41.169591	52.105263	19.649123	7.463158	41.461988
std	4.501683	0.0	0.573916	1.166442	6.251005e+07	1.298837	0.576558	1.109710	0.984473	0.781599	0.398043	25.976744	28.827898	22.194192	32.815766	39.079949	39.804315	2.706840	15.038000
min	36.000000	3.0	1.000000	1.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	5.555556
25%	40.500000	3.0	1.000000	2.000000	3.000000e+05	1.000000	0.000000	2.000000	0.000000	0.000000	0.000000	20.000000	0.000000	40.000000	0.000000	0.000000	0.000000	5.000000	27.777778
50%	40.500000	3.0	1.000000	2.000000	2.000000e+06	2.000000	0.000000	2.000000	1.000000	1.000000	0.000000	40.000000	0.000000	40.000000	33.333333	50.000000	0.000000	7.000000	38.888889
75%	44.000000	3.0	2.000000	3.000000	7.500000e+06	3.000000	1.000000	3.000000	2.000000	2.000000	0.000000	60.000000	50.000000	60.000000	66.666667	100.000000	0.000000	9.000000	50.000000
max	54.000000	3.0	3.000000	5.000000	6.000000e+08	5.000000	2.000000	5.000000	3.000000	2.000000	1.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	15.000000	83.333333

	ResponseId
isced
1	531
2	417
3	65

	age	age_rec	isced	income	wealth	Certainty_5	Uncertainty_2	RiskComprehension_5	GraphLiteracy_3	Numeracy_2	Bayesianreasoning_1	Certainty_%	Uncertainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_18	TotalScore_%
count	34.000000	34.0	34.000000	34.000000	3.400000e+01	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000
mean	62.897059	4.0	1.500000	2.176471	1.032985e+07	2.382353	0.441176	2.323529	1.529412	1.117647	0.235294	47.647059	22.058824	46.470588	50.980392	55.882353	23.529412	8.029412	44.607843
std	4.344588	0.0	0.615457	1.217812	1.451734e+07	0.953930	0.612554	1.296162	0.928841	0.769286	0.430562	19.078597	30.627717	25.923238	30.961358	38.464281	43.056155	2.587678	14.375990
min	57.000000	4.0	1.000000	1.000000	1.000000e+05	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	20.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.000000	16.666667
25%	60.500000	4.0	1.000000	1.000000	1.437500e+06	2.000000	0.000000	1.000000	1.000000	1.000000	0.000000	40.000000	0.000000	20.000000	33.333333	50.000000	0.000000	6.000000	33.333333
50%	60.500000	4.0	1.000000	2.000000	6.000000e+06	2.000000	0.000000	2.000000	2.000000	1.000000	0.000000	40.000000	0.000000	40.000000	66.666667	50.000000	0.000000	7.500000	41.666667
75%	64.250000	4.0	2.000000	2.000000	1.000000e+07	3.000000	1.000000	3.000000	2.000000	2.000000	0.000000	60.000000	50.000000	60.000000	66.666667	100.000000	0.000000	10.000000	55.555556
max	70.500000	4.0	3.000000	5.000000	5.250000e+07	4.000000	2.000000	5.000000	3.000000	2.000000	1.000000	80.000000	100.000000	100.000000	100.000000	100.000000	100.000000	13.000000	72.222222

	age	age_rec	isced	income	wealth	Certainty_5	Uncertainty_2	RiskComprehension_5	GraphLiteracy_3	Numeracy_2	Bayesianreasoning_1	Certainty_%	Uncertainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_18	TotalScore_%
count	338.000000	338.000000	338.000000	338.000000	3.380000e+02	338.000000	338.000000	338.000000	338.000000	338.000000	338.000000	338.000000	338.000000	338.000000	338.000000	338.000000	338.000000	338.000000	338.000000
mean	23.139053	1.994083	1.837278	1.825444	7.068495e+06	2.970414	0.520710	2.183432	1.423077	1.136095	0.257396	59.408284	26.035503	43.668639	47.435897	56.804734	25.739645	8.491124	47.172913
std	3.331116	0.108786	0.630694	1.037432	3.523029e+07	1.253723	0.659005	0.987499	0.981279	0.800111	0.437848	25.074468	32.950263	19.749987	32.709292	40.005530	43.784781	2.547165	14.150919
min	16.000000	0.000000	1.000000	1.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	2.000000	11.111111
25%	21.500000	2.000000	1.000000	1.000000	1.000000e+05	2.000000	0.000000	2.000000	1.000000	0.000000	0.000000	40.000000	0.000000	40.000000	33.333333	0.000000	0.000000	7.000000	38.888889
50%	21.500000	2.000000	2.000000	2.000000	5.000000e+05	4.000000	0.000000	2.000000	1.000000	1.000000	0.000000	80.000000	0.000000	40.000000	33.333333	50.000000	0.000000	9.000000	50.000000
75%	26.000000	2.000000	2.000000	2.000000	2.000000e+06	4.000000	1.000000	3.000000	2.000000	2.000000	1.000000	80.000000	50.000000	60.000000	66.666667	100.000000	100.000000	10.000000	55.555556
max	30.000000	2.000000	3.000000	5.000000	5.000000e+08	4.000000	2.000000	5.000000	3.000000	2.000000	1.000000	80.000000	100.000000	100.000000	100.000000	100.000000	100.000000	15.000000	83.333333

	age	age_rec	isced	income	wealth	Certainty_5	Uncertainty_2	RiskComprehension_5	GraphLiteracy_3	Numeracy_2	Bayesianreasoning_1	Certainty_%	Uncertainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_18	TotalScore_%
count	675.000000	675.000000	675.000000	675.000000	6.750000e+02	675.000000	675.000000	675.000000	675.000000	675.000000	675.000000	675.000000	675.000000	675.000000	675.000000	675.000000	675.000000	675.000000	675.000000
mean	37.421481	2.522963	1.391111	2.343704	1.087433e+07	2.139259	0.422222	2.198519	1.247407	1.014815	0.202963	42.785185	21.111111	43.970370	41.580247	50.740741	20.296296	7.225185	40.139918
std	8.757857	0.592227	0.548462	1.127128	4.645892e+07	1.275720	0.587540	1.137047	0.974210	0.789256	0.402503	25.514407	29.376979	22.740942	32.473656	39.462818	40.250343	2.611255	14.506972
min	30.500000	2.000000	1.000000	1.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	30.500000	2.000000	1.000000	2.000000	3.775000e+05	1.000000	0.000000	1.000000	0.000000	0.000000	0.000000	20.000000	0.000000	20.000000	0.000000	0.000000	0.000000	5.000000	27.777778
50%	34.000000	2.000000	1.000000	2.000000	1.500000e+06	2.000000	0.000000	2.000000	1.000000	1.000000	0.000000	40.000000	0.000000	40.000000	33.333333	50.000000	0.000000	7.000000	38.888889
75%	40.500000	3.000000	2.000000	3.000000	6.000000e+06	3.000000	1.000000	3.000000	2.000000	2.000000	0.000000	60.000000	50.000000	60.000000	66.666667	100.000000	0.000000	9.000000	50.000000
max	70.500000	4.000000	3.000000	5.000000	6.000000e+08	5.000000	2.000000	5.000000	3.000000	2.000000	1.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	15.000000	83.333333

	ResponseId	age	age_rec	isced	income	wealth	Certainty_5	Uncertainty_2	RiskComprehension_5	GraphLiteracy_3	Numeracy_2	Bayesianreasoning_1	Certainty_%	Uncertainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_18	TotalScore_%	Quartile Number
0	2rkur9ckjvsyz36zmayz1zw2rkur9ckn	40.5	3	1	3	0.0	3	0	2	0	0	0	60.0	0.0	40.0	0.000000	0.0	0.0	5	27.777778	1
1	R_4DPGUOAP86yO30R	19.0	2	3	1	0.0	4	2	1	2	1	1	80.0	100.0	20.0	66.666667	50.0	100.0	11	61.111111	1
2	R_4fNO2Z5GF8KerZI	24.0	2	2	2	0.0	4	0	4	1	2	0	80.0	0.0	80.0	33.333333	100.0	0.0	11	61.111111	1
3	R_4lnmGavSf6rw1eE	31.0	2	1	1	0.0	1	0	2	1	2	0	20.0	0.0	40.0	33.333333	100.0	0.0	6	33.333333	1
4	y139mcxc619h0ooosy139muk14mpfpvm	30.5	2	3	1	0.0	5	0	2	0	0	1	100.0	0.0	40.0	0.000000	0.0	100.0	8	44.444444	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1008	35zux4sc18rplyz3dc85z35zux4sdfm4	21.5	2	2	4	500000000.0	2	0	1	0	1	0	40.0	0.0	20.0	0.000000	50.0	0.0	4	22.222222	4
1009	wn9mk6m1k8o10l1twn9mk6b4z7l7yvhc	50.5	3	1	3	500000000.0	2	0	3	3	2	0	40.0	0.0	60.0	100.000000	100.0	0.0	10	55.555556	4
1010	4udfypw7lf8mmgjmxr4d4udfym4sx1sf	40.5	3	1	3	500000000.0	0	0	3	0	0	0	0.0	0.0	60.0	0.000000	0.0	0.0	3	16.666667	4
1011	oczyh23wpxpk4o0teoczy5zzzng3i6kk	30.5	2	2	5	500000000.0	1	0	3	3	2	0	20.0	0.0	60.0	100.000000	100.0	0.0	9	50.000000	4
1012	zcj7ldokhyo6217f9sriwizcj7ldodwt	40.5	3	2	3	600000000.0	3	1	2	1	2	1	60.0	50.0	40.0	33.333333	100.0	100.0	10	55.555556	4